[1] "ABCanalysis" "kbv" "reshape2" "mice" "reticulate"
[6] "missForest" "BiocManager" "forecast" "janitor" "lubridate"
[11] "forcats" "stringr" "dplyr" "purrr" "readr"
[16] "tidyr" "tibble" "ggplot2" "tidyverse" "quarto"
[21] "JuliaCall" "pacman" quarto_project_27012025
learn r julia & python
test Quarto
R setup
loading dataset
sepal_length sepal_width petal_length petal_width species
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3.0 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 NA NA NA NA setosa
5 NA 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosa
7 4.6 3.4 1.4 NA setosa
8 5.0 3.4 NA 0.2 setosa
9 4.4 2.9 1.4 0.2 <NA>
10 4.9 3.1 1.5 0.1 <NA>
11 5.4 3.7 1.5 NA setosa
12 4.8 3.4 1.6 0.2 setosa
13 4.8 NA 1.4 0.1 setosa
14 4.3 3.0 1.1 NA setosa
15 5.8 4.0 1.2 NA setosa
16 5.7 NA 1.5 0.4 setosa
17 5.4 3.9 1.3 0.4 setosa
18 5.1 NA 1.4 0.3 <NA>
19 5.7 3.8 NA 0.3 setosa
20 5.1 3.8 1.5 0.3 setosa
21 5.4 3.4 1.7 0.2 setosa
22 5.1 3.7 1.5 0.4 setosa
23 4.6 NA 1.0 0.2 setosa
24 5.1 3.3 1.7 0.5 setosa
25 4.8 3.4 1.9 0.2 setosa
26 5.0 3.0 1.6 0.2 setosa
27 NA 3.4 1.6 0.4 setosa
28 5.2 3.5 1.5 0.2 <NA>
29 5.2 3.4 1.4 0.2 setosa
30 4.7 3.2 1.6 NA setosa
31 4.8 3.1 1.6 0.2 setosa
32 5.4 3.4 1.5 0.4 setosa
33 5.2 4.1 1.5 0.1 setosa
34 5.5 4.2 1.4 0.2 setosa
35 4.9 3.1 1.5 0.2 setosa
36 NA NA NA 0.2 setosa
37 5.5 3.5 1.3 0.2 setosa
38 4.9 3.6 1.4 0.1 <NA>
39 4.4 3.0 1.3 0.2 <NA>
40 5.1 3.4 1.5 0.2 setosa
41 5.0 3.5 1.3 0.3 setosa
42 4.5 2.3 1.3 0.3 setosa
43 4.4 3.2 1.3 0.2 setosa
44 5.0 NA 1.6 0.6 setosa
45 5.1 3.8 1.9 NA setosa
46 4.8 3.0 1.4 0.3 <NA>
47 5.1 3.8 1.6 0.2 setosa
48 4.6 3.2 1.4 0.2 setosa
49 5.3 3.7 1.5 NA setosa
50 5.0 3.3 1.4 0.2 setosa
51 7.0 3.2 4.7 1.4 versicolor
52 6.4 3.2 4.5 1.5 versicolor
53 6.9 3.1 4.9 NA versicolor
54 5.5 2.3 4.0 1.3 versicolor
55 6.5 2.8 4.6 1.5 versicolor
56 5.7 2.8 4.5 1.3 versicolor
57 6.3 NA 4.7 1.6 versicolor
58 4.9 2.4 3.3 1.0 versicolor
59 6.6 2.9 4.6 1.3 <NA>
60 5.2 2.7 3.9 1.4 versicolor
61 5.0 2.0 NA 1.0 versicolor
62 5.9 3.0 4.2 1.5 versicolor
63 6.0 2.2 4.0 1.0 <NA>
64 6.1 2.9 4.7 1.4 versicolor
65 5.6 2.9 3.6 1.3 versicolor
66 6.7 3.1 4.4 1.4 versicolor
67 5.6 3.0 4.5 1.5 versicolor
68 5.8 2.7 4.1 1.0 versicolor
69 6.2 2.2 4.5 1.5 versicolor
70 5.6 2.5 3.9 1.1 versicolor
71 5.9 3.2 4.8 1.8 versicolor
72 6.1 2.8 4.0 1.3 versicolor
73 NA 2.5 NA 1.5 <NA>
74 6.1 2.8 4.7 NA versicolor
75 6.4 2.9 4.3 1.3 versicolor
76 6.6 3.0 4.4 1.4 versicolor
77 6.8 2.8 NA 1.4 versicolor
78 6.7 3.0 5.0 1.7 versicolor
79 6.0 2.9 4.5 1.5 versicolor
80 NA 2.6 3.5 1.0 versicolor
81 5.5 2.4 3.8 1.1 versicolor
82 5.5 2.4 3.7 1.0 versicolor
83 5.8 2.7 3.9 1.2 versicolor
84 6.0 NA 5.1 1.6 versicolor
85 5.4 3.0 4.5 1.5 <NA>
86 6.0 3.4 4.5 1.6 versicolor
87 6.7 NA 4.7 1.5 <NA>
88 6.3 2.3 4.4 1.3 versicolor
89 5.6 3.0 4.1 1.3 <NA>
90 5.5 2.5 4.0 1.3 versicolor
91 5.5 2.6 NA NA versicolor
92 6.1 3.0 4.6 1.4 versicolor
93 5.8 2.6 NA 1.2 versicolor
94 5.0 2.3 3.3 1.0 versicolor
95 5.6 NA 4.2 1.3 <NA>
96 5.7 3.0 4.2 1.2 versicolor
97 5.7 2.9 4.2 1.3 versicolor
98 6.2 2.9 NA 1.3 <NA>
99 5.1 2.5 3.0 1.1 versicolor
100 5.7 2.8 4.1 1.3 versicolor
101 6.3 3.3 6.0 2.5 virginica
102 5.8 2.7 5.1 1.9 virginica
103 7.1 3.0 5.9 NA virginica
104 NA 2.9 5.6 NA virginica
105 6.5 3.0 5.8 2.2 virginica
106 NA 3.0 6.6 2.1 virginica
107 4.9 2.5 4.5 1.7 virginica
108 7.3 2.9 6.3 1.8 virginica
109 6.7 2.5 NA 1.8 virginica
110 7.2 3.6 6.1 2.5 virginica
111 6.5 3.2 5.1 2.0 virginica
112 6.4 2.7 5.3 1.9 virginica
113 6.8 3.0 5.5 2.1 virginica
114 5.7 2.5 5.0 2.0 virginica
115 5.8 NA 5.1 2.4 virginica
116 6.4 3.2 5.3 NA virginica
117 NA 3.0 5.5 1.8 virginica
118 NA 3.8 6.7 2.2 virginica
119 7.7 2.6 6.9 2.3 virginica
120 6.0 2.2 NA 1.5 virginica
121 6.9 3.2 5.7 2.3 virginica
122 5.6 2.8 4.9 NA virginica
123 7.7 2.8 6.7 2.0 virginica
124 6.3 2.7 4.9 1.8 virginica
125 NA 3.3 5.7 2.1 virginica
126 7.2 3.2 NA 1.8 virginica
127 6.2 2.8 4.8 1.8 <NA>
128 6.1 3.0 4.9 1.8 virginica
129 6.4 2.8 5.6 2.1 virginica
130 7.2 3.0 5.8 1.6 <NA>
131 NA 2.8 6.1 1.9 virginica
132 NA 3.8 6.4 2.0 virginica
133 6.4 2.8 5.6 2.2 virginica
134 6.3 2.8 5.1 1.5 <NA>
135 NA 2.6 5.6 1.4 virginica
136 7.7 3.0 6.1 2.3 virginica
137 6.3 3.4 5.6 2.4 virginica
138 6.4 3.1 5.5 1.8 virginica
139 NA 3.0 4.8 1.8 virginica
140 6.9 3.1 5.4 2.1 virginica
141 6.7 3.1 5.6 2.4 virginica
142 6.9 3.1 5.1 2.3 virginica
143 5.8 2.7 5.1 1.9 virginica
144 6.8 3.2 5.9 2.3 virginica
145 6.7 3.3 5.7 2.5 virginica
146 6.7 3.0 5.2 2.3 virginica
147 6.3 2.5 5.0 NA virginica
148 6.5 3.0 5.2 2.0 virginica
149 6.2 3.4 NA 2.3 virginica
150 5.9 3.0 5.1 1.8 virginica'data.frame': 150 obs. of 5 variables:
$ sepal_length: num 5.1 4.9 4.7 NA NA 5.4 4.6 5 4.4 4.9 ...
$ sepal_width : num 3.5 3 3.2 NA 3.6 3.9 3.4 3.4 2.9 3.1 ...
$ petal_length: num 1.4 1.4 1.3 NA 1.4 1.7 1.4 NA 1.4 1.5 ...
$ petal_width : num 0.2 0.2 0.2 NA 0.2 0.4 NA 0.2 0.2 0.1 ...
$ species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 NA NA ...sepal_length sepal_width petal_length petal_width species
15 12 14 16 18 [1] 0 0 0 4 1 0 1 1 1 1 1 0 1 1 1 1 0 2 1 0 0 0 1 0 0 0 1 1 0 1 0 0 0 0 0 3 0
[38] 1 1 0 0 0 0 1 1 1 0 0 1 0 0 0 1 0 0 0 1 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 3 1
[75] 0 0 1 0 0 1 0 0 0 1 1 0 2 0 1 0 2 0 1 0 2 0 0 2 0 0 0 0 1 2 0 1 0 0 1 0 0
[112] 0 0 0 1 1 1 1 0 1 0 1 0 0 1 1 1 0 0 1 1 1 0 1 1 0 0 0 1 0 0 0 0 0 0 0 1 0
[149] 1 0forecasting with r
[1] "BAJAJHIND.NS"
removing missing values using mice package
iter imp variable
1 1 sepal_length sepal_width petal_length petal_width species
1 2 sepal_length sepal_width petal_length petal_width species
1 3 sepal_length sepal_width petal_length petal_width species
1 4 sepal_length sepal_width petal_length petal_width species
1 5 sepal_length sepal_width petal_length petal_width species
2 1 sepal_length sepal_width petal_length petal_width species
2 2 sepal_length sepal_width petal_length petal_width species
2 3 sepal_length sepal_width petal_length petal_width species
2 4 sepal_length sepal_width petal_length petal_width species
2 5 sepal_length sepal_width petal_length petal_width species
3 1 sepal_length sepal_width petal_length petal_width species
3 2 sepal_length sepal_width petal_length petal_width species
3 3 sepal_length sepal_width petal_length petal_width species
3 4 sepal_length sepal_width petal_length petal_width species
3 5 sepal_length sepal_width petal_length petal_width species
4 1 sepal_length sepal_width petal_length petal_width species
4 2 sepal_length sepal_width petal_length petal_width species
4 3 sepal_length sepal_width petal_length petal_width species
4 4 sepal_length sepal_width petal_length petal_width species
4 5 sepal_length sepal_width petal_length petal_width species
5 1 sepal_length sepal_width petal_length petal_width species
5 2 sepal_length sepal_width petal_length petal_width species
5 3 sepal_length sepal_width petal_length petal_width species
5 4 sepal_length sepal_width petal_length petal_width species
5 5 sepal_length sepal_width petal_length petal_width species sepal_length sepal_width petal_length petal_width species
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3.0 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.4 2.8 1.3 0.2 setosa
5 5.0 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosa
7 4.6 3.4 1.4 0.4 setosa
8 5.0 3.4 1.9 0.2 setosa
9 4.4 2.9 1.4 0.2 setosa
10 4.9 3.1 1.5 0.1 setosa
11 5.4 3.7 1.5 0.3 setosa
12 4.8 3.4 1.6 0.2 setosa
13 4.8 3.2 1.4 0.1 setosa
14 4.3 3.0 1.1 0.2 setosa
15 5.8 4.0 1.2 0.1 setosa
16 5.7 3.5 1.5 0.4 setosa
17 5.4 3.9 1.3 0.4 setosa
18 5.1 3.0 1.4 0.3 setosa
19 5.7 3.8 1.7 0.3 setosa
20 5.1 3.8 1.5 0.3 setosa
21 5.4 3.4 1.7 0.2 setosa
22 5.1 3.7 1.5 0.4 setosa
23 4.6 3.1 1.0 0.2 setosa
24 5.1 3.3 1.7 0.5 setosa
25 4.8 3.4 1.9 0.2 setosa
26 5.0 3.0 1.6 0.2 setosa
27 4.9 3.4 1.6 0.4 setosa
28 5.2 3.5 1.5 0.2 setosa
29 5.2 3.4 1.4 0.2 setosa
30 4.7 3.2 1.6 0.2 setosa
31 4.8 3.1 1.6 0.2 setosa
32 5.4 3.4 1.5 0.4 setosa
33 5.2 4.1 1.5 0.1 setosa
34 5.5 4.2 1.4 0.2 setosa
35 4.9 3.1 1.5 0.2 setosa
36 5.4 3.0 1.4 0.2 setosa
37 5.5 3.5 1.3 0.2 setosa
38 4.9 3.6 1.4 0.1 setosa
39 4.4 3.0 1.3 0.2 setosa
40 5.1 3.4 1.5 0.2 setosa
41 5.0 3.5 1.3 0.3 setosa
42 4.5 2.3 1.3 0.3 setosa
43 4.4 3.2 1.3 0.2 setosa
44 5.0 3.7 1.6 0.6 setosa
45 5.1 3.8 1.9 0.2 setosa
46 4.8 3.0 1.4 0.3 setosa
47 5.1 3.8 1.6 0.2 setosa
48 4.6 3.2 1.4 0.2 setosa
49 5.3 3.7 1.5 0.2 setosa
50 5.0 3.3 1.4 0.2 setosa
51 7.0 3.2 4.7 1.4 versicolor
52 6.4 3.2 4.5 1.5 versicolor
53 6.9 3.1 4.9 1.7 versicolor
54 5.5 2.3 4.0 1.3 versicolor
55 6.5 2.8 4.6 1.5 versicolor
56 5.7 2.8 4.5 1.3 versicolor
57 6.3 3.2 4.7 1.6 versicolor
58 4.9 2.4 3.3 1.0 versicolor
59 6.6 2.9 4.6 1.3 versicolor
60 5.2 2.7 3.9 1.4 versicolor
61 5.0 2.0 3.3 1.0 versicolor
62 5.9 3.0 4.2 1.5 versicolor
63 6.0 2.2 4.0 1.0 versicolor
64 6.1 2.9 4.7 1.4 versicolor
65 5.6 2.9 3.6 1.3 versicolor
66 6.7 3.1 4.4 1.4 versicolor
67 5.6 3.0 4.5 1.5 versicolor
68 5.8 2.7 4.1 1.0 versicolor
69 6.2 2.2 4.5 1.5 versicolor
70 5.6 2.5 3.9 1.1 versicolor
71 5.9 3.2 4.8 1.8 versicolor
72 6.1 2.8 4.0 1.3 versicolor
73 5.8 2.5 4.9 1.5 virginica
74 6.1 2.8 4.7 1.5 versicolor
75 6.4 2.9 4.3 1.3 versicolor
76 6.6 3.0 4.4 1.4 versicolor
77 6.8 2.8 4.6 1.4 versicolor
78 6.7 3.0 5.0 1.7 versicolor
79 6.0 2.9 4.5 1.5 versicolor
80 5.0 2.6 3.5 1.0 versicolor
81 5.5 2.4 3.8 1.1 versicolor
82 5.5 2.4 3.7 1.0 versicolor
83 5.8 2.7 3.9 1.2 versicolor
84 6.0 2.7 5.1 1.6 versicolor
85 5.4 3.0 4.5 1.5 versicolor
86 6.0 3.4 4.5 1.6 versicolor
87 6.7 3.2 4.7 1.5 versicolor
88 6.3 2.3 4.4 1.3 versicolor
89 5.6 3.0 4.1 1.3 versicolor
90 5.5 2.5 4.0 1.3 versicolor
91 5.5 2.6 3.9 1.3 versicolor
92 6.1 3.0 4.6 1.4 versicolor
93 5.8 2.6 4.2 1.2 versicolor
94 5.0 2.3 3.3 1.0 versicolor
95 5.6 2.6 4.2 1.3 versicolor
96 5.7 3.0 4.2 1.2 versicolor
97 5.7 2.9 4.2 1.3 versicolor
98 6.2 2.9 4.7 1.3 versicolor
99 5.1 2.5 3.0 1.1 versicolor
100 5.7 2.8 4.1 1.3 versicolor
101 6.3 3.3 6.0 2.5 virginica
102 5.8 2.7 5.1 1.9 virginica
103 7.1 3.0 5.9 2.3 virginica
104 6.7 2.9 5.6 2.4 virginica
105 6.5 3.0 5.8 2.2 virginica
106 7.7 3.0 6.6 2.1 virginica
107 4.9 2.5 4.5 1.7 virginica
108 7.3 2.9 6.3 1.8 virginica
109 6.7 2.5 5.6 1.8 virginica
110 7.2 3.6 6.1 2.5 virginica
111 6.5 3.2 5.1 2.0 virginica
112 6.4 2.7 5.3 1.9 virginica
113 6.8 3.0 5.5 2.1 virginica
114 5.7 2.5 5.0 2.0 virginica
115 5.8 3.2 5.1 2.4 virginica
116 6.4 3.2 5.3 2.2 virginica
117 6.3 3.0 5.5 1.8 virginica
118 7.2 3.8 6.7 2.2 virginica
119 7.7 2.6 6.9 2.3 virginica
120 6.0 2.2 5.0 1.5 virginica
121 6.9 3.2 5.7 2.3 virginica
122 5.6 2.8 4.9 1.5 virginica
123 7.7 2.8 6.7 2.0 virginica
124 6.3 2.7 4.9 1.8 virginica
125 6.3 3.3 5.7 2.1 virginica
126 7.2 3.2 5.4 1.8 virginica
127 6.2 2.8 4.8 1.8 versicolor
128 6.1 3.0 4.9 1.8 virginica
129 6.4 2.8 5.6 2.1 virginica
130 7.2 3.0 5.8 1.6 virginica
131 7.7 2.8 6.1 1.9 virginica
132 7.2 3.8 6.4 2.0 virginica
133 6.4 2.8 5.6 2.2 virginica
134 6.3 2.8 5.1 1.5 versicolor
135 6.7 2.6 5.6 1.4 virginica
136 7.7 3.0 6.1 2.3 virginica
137 6.3 3.4 5.6 2.4 virginica
138 6.4 3.1 5.5 1.8 virginica
139 6.4 3.0 4.8 1.8 virginica
140 6.9 3.1 5.4 2.1 virginica
141 6.7 3.1 5.6 2.4 virginica
142 6.9 3.1 5.1 2.3 virginica
143 5.8 2.7 5.1 1.9 virginica
144 6.8 3.2 5.9 2.3 virginica
145 6.7 3.3 5.7 2.5 virginica
146 6.7 3.0 5.2 2.3 virginica
147 6.3 2.5 5.0 2.0 virginica
148 6.5 3.0 5.2 2.0 virginica
149 6.2 3.4 5.3 2.3 virginica
150 5.9 3.0 5.1 1.8 virginicasepal_length sepal_width petal_length petal_width species
0 0 0 0 0 [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[38] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[75] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[112] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[149] 0 0# A tibble: 3 × 2
species cnt
<fct> <int>
1 setosa 50
2 versicolor 51
3 virginica 49removing outliers
sepal_length sepal_width petal_length petal_width
Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100
1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300
Median :5.800 Median :3.000 Median :4.350 Median :1.300
Mean :5.836 Mean :3.046 Mean :3.753 Mean :1.205
3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800
Max. :7.700 Max. :4.200 Max. :6.900 Max. :2.500
species
setosa :50
versicolor:51
virginica :49
sepal_length sepal_width petal_length petal_width
[1,] 5.1 3.5 1.4 0.2
[2,] 4.9 3.0 1.4 0.2
[3,] 4.7 3.2 1.3 0.2
[4,] 4.4 2.8 1.3 0.2
[5,] 5.0 3.6 1.4 0.2
[6,] 5.4 3.9 1.7 0.4
[7,] 4.6 3.4 1.4 0.4
[8,] 5.0 3.4 1.9 0.2
[9,] 4.4 2.9 1.4 0.2
[10,] 4.9 3.1 1.5 0.1
[11,] 5.4 3.7 1.5 0.3
[12,] 4.8 3.4 1.6 0.2
[13,] 4.8 3.2 1.4 0.1
[14,] 4.3 3.0 1.1 0.2
[15,] 5.8 4.0 1.2 0.1
[16,] 5.7 3.5 1.5 0.4
[17,] 5.4 3.9 1.3 0.4
[18,] 5.1 3.0 1.4 0.3
[19,] 5.7 3.8 1.7 0.3
[20,] 5.1 3.8 1.5 0.3
[21,] 5.4 3.4 1.7 0.2
[22,] 5.1 3.7 1.5 0.4
[23,] 4.6 3.1 1.0 0.2
[24,] 5.1 3.3 1.7 0.5
[25,] 4.8 3.4 1.9 0.2
[26,] 5.0 3.0 1.6 0.2
[27,] 4.9 3.4 1.6 0.4
[28,] 5.2 3.5 1.5 0.2
[29,] 5.2 3.4 1.4 0.2
[30,] 4.7 3.2 1.6 0.2
[31,] 4.8 3.1 1.6 0.2
[32,] 5.4 3.4 1.5 0.4
[33,] 5.2 2.8 1.5 0.1
[34,] 5.5 2.8 1.4 0.2
[35,] 4.9 3.1 1.5 0.2
[36,] 5.4 3.0 1.4 0.2
[37,] 5.5 3.5 1.3 0.2
[38,] 4.9 3.6 1.4 0.1
[39,] 4.4 3.0 1.3 0.2
[40,] 5.1 3.4 1.5 0.2
[41,] 5.0 3.5 1.3 0.3
[42,] 4.5 2.3 1.3 0.3
[43,] 4.4 3.2 1.3 0.2
[44,] 5.0 3.7 1.6 0.6
[45,] 5.1 3.8 1.9 0.2
[46,] 4.8 3.0 1.4 0.3
[47,] 5.1 3.8 1.6 0.2
[48,] 4.6 3.2 1.4 0.2
[49,] 5.3 3.7 1.5 0.2
[50,] 5.0 3.3 1.4 0.2
[51,] 7.0 3.2 4.7 1.4
[52,] 6.4 3.2 4.5 1.5
[53,] 6.9 3.1 4.9 1.7
[54,] 5.5 2.3 4.0 1.3
[55,] 6.5 2.8 4.6 1.5
[56,] 5.7 2.8 4.5 1.3
[57,] 6.3 3.2 4.7 1.6
[58,] 4.9 2.4 3.3 1.0
[59,] 6.6 2.9 4.6 1.3
[60,] 5.2 2.7 3.9 1.4
[61,] 5.0 2.8 3.3 1.0
[62,] 5.9 3.0 4.2 1.5
[63,] 6.0 2.2 4.0 1.0
[64,] 6.1 2.9 4.7 1.4
[65,] 5.6 2.9 3.6 1.3
[66,] 6.7 3.1 4.4 1.4
[67,] 5.6 3.0 4.5 1.5
[68,] 5.8 2.7 4.1 1.0
[69,] 6.2 2.2 4.5 1.5
[70,] 5.6 2.5 3.9 1.1
[71,] 5.9 3.2 4.8 1.8
[72,] 6.1 2.8 4.0 1.3
[73,] 5.8 2.5 4.9 1.5
[74,] 6.1 2.8 4.7 1.5
[75,] 6.4 2.9 4.3 1.3
[76,] 6.6 3.0 4.4 1.4
[77,] 6.8 2.8 4.6 1.4
[78,] 6.7 3.0 5.0 1.7
[79,] 6.0 2.9 4.5 1.5
[80,] 5.0 2.6 3.5 1.0
[81,] 5.5 2.4 3.8 1.1
[82,] 5.5 2.4 3.7 1.0
[83,] 5.8 2.7 3.9 1.2
[84,] 6.0 2.7 5.1 1.6
[85,] 5.4 3.0 4.5 1.5
[86,] 6.0 3.4 4.5 1.6
[87,] 6.7 3.2 4.7 1.5
[88,] 6.3 2.3 4.4 1.3
[89,] 5.6 3.0 4.1 1.3
[90,] 5.5 2.5 4.0 1.3
[91,] 5.5 2.6 3.9 1.3
[92,] 6.1 3.0 4.6 1.4
[93,] 5.8 2.6 4.2 1.2
[94,] 5.0 2.3 3.3 1.0
[95,] 5.6 2.6 4.2 1.3
[96,] 5.7 3.0 4.2 1.2
[97,] 5.7 2.9 4.2 1.3
[98,] 6.2 2.9 4.7 1.3
[99,] 5.1 2.5 3.0 1.1
[100,] 5.7 2.8 4.1 1.3
[101,] 6.3 3.3 6.0 2.5
[102,] 5.8 2.7 5.1 1.9
[103,] 7.1 3.0 5.9 2.3
[104,] 6.7 2.9 5.6 2.4
[105,] 6.5 3.0 5.8 2.2
[106,] 7.7 3.0 6.6 2.1
[107,] 4.9 2.5 4.5 1.7
[108,] 7.3 2.9 6.3 1.8
[109,] 6.7 2.5 5.6 1.8
[110,] 7.2 3.6 6.1 2.5
[111,] 6.5 3.2 5.1 2.0
[112,] 6.4 2.7 5.3 1.9
[113,] 6.8 3.0 5.5 2.1
[114,] 5.7 2.5 5.0 2.0
[115,] 5.8 3.2 5.1 2.4
[116,] 6.4 3.2 5.3 2.2
[117,] 6.3 3.0 5.5 1.8
[118,] 7.2 3.8 6.7 2.2
[119,] 7.7 2.6 6.9 2.3
[120,] 6.0 2.2 5.0 1.5
[121,] 6.9 3.2 5.7 2.3
[122,] 5.6 2.8 4.9 1.5
[123,] 7.7 2.8 6.7 2.0
[124,] 6.3 2.7 4.9 1.8
[125,] 6.3 3.3 5.7 2.1
[126,] 7.2 3.2 5.4 1.8
[127,] 6.2 2.8 4.8 1.8
[128,] 6.1 3.0 4.9 1.8
[129,] 6.4 2.8 5.6 2.1
[130,] 7.2 3.0 5.8 1.6
[131,] 7.7 2.8 6.1 1.9
[132,] 7.2 3.8 6.4 2.0
[133,] 6.4 2.8 5.6 2.2
[134,] 6.3 2.8 5.1 1.5
[135,] 6.7 2.6 5.6 1.4
[136,] 7.7 3.0 6.1 2.3
[137,] 6.3 3.4 5.6 2.4
[138,] 6.4 3.1 5.5 1.8
[139,] 6.4 3.0 4.8 1.8
[140,] 6.9 3.1 5.4 2.1
[141,] 6.7 3.1 5.6 2.4
[142,] 6.9 3.1 5.1 2.3
[143,] 5.8 2.7 5.1 1.9
[144,] 6.8 3.2 5.9 2.3
[145,] 6.7 3.3 5.7 2.5
[146,] 6.7 3.0 5.2 2.3
[147,] 6.3 2.5 5.0 2.0
[148,] 6.5 3.0 5.2 2.0
[149,] 6.2 3.4 5.3 2.3
[150,] 5.9 3.0 5.1 1.8variable importance
Boruta performed 9 iterations in 0.3238409 secs.
4 attributes confirmed important: petal_length, petal_width,
sepal_length, sepal_width;
No attributes deemed unimportant. Length Class Mode
finalDecision 4 factor numeric
ImpHistory 63 -none- numeric
pValue 1 -none- numeric
maxRuns 1 -none- numeric
light 1 -none- logical
mcAdj 1 -none- logical
timeTaken 1 difftime numeric
roughfixed 1 -none- logical
call 3 -none- call
impSource 1 -none- character meanImp medianImp minImp maxImp normHits decision
petal_length 32.26500 32.57539 30.92889 33.56493 1 Confirmed
petal_width 30.35949 30.11637 28.98502 32.10968 1 Confirmed
sepal_length 15.97722 15.96015 15.30470 17.01646 1 Confirmed
sepal_width 10.54620 10.65407 9.27892 11.15894 1 ConfirmedLinear regression using R
x y
1 1 93.34333
2 2 101.59069
3 3 105.14347
4 4 108.94041
5 5 114.74824
6 6 116.43280
7 7 114.42546
8 8 120.10295
9 9 127.67475
10 10 129.97826 x y yp
1 1 93.34333 96.7315
2 2 101.59069 100.3996
3 3 105.14347 104.0677
4 4 108.94041 107.7359
5 5 114.74824 111.4040
6 6 116.43280 115.0721
7 7 114.42546 118.7402
8 8 120.10295 122.4083
9 9 127.67475 126.0765
10 10 129.97826 129.7446 x variable value
1 1 y 93.34333
2 2 y 101.59069
3 3 y 105.14347
4 4 y 108.94041
5 5 y 114.74824
6 6 y 116.43280
7 7 y 114.42546
8 8 y 120.10295
9 9 y 127.67475
10 10 y 129.97826
11 1 yp 96.73150
12 2 yp 100.39962
13 3 yp 104.06774
14 4 yp 107.73586
15 5 yp 111.40398
16 6 yp 115.07210
17 7 yp 118.74022
18 8 yp 122.40834
19 9 yp 126.07646
20 10 yp 129.74458
histogram with normal density curve in R
abc analysis using Random
code name unitprice
1 A AAAAAAAAAA 976
2 B BBBBBBBBBB 898
3 C CCCCCCCCCC 786
4 D DDDDDDDDDD 488
5 E EEEEEEEEEE 401 code qty
1 A 21
2 B 9
3 A 10
4 A 21
5 A 19
6 E 17
7 B 13
8 C 22
9 D 22
10 A 14
11 D 21
12 D 15
13 E 6
14 B 25
15 A 11
16 A 18
17 E 17
18 C 9
19 A 8
20 E 16
21 E 5
22 D 15
23 A 11
24 B 11
25 E 23
26 A 7
27 D 23
28 D 18
29 B 8
30 A 13 code name unitprice qty cost
1 A AAAAAAAAAA 976 21 20496
2 A AAAAAAAAAA 976 10 9760
3 A AAAAAAAAAA 976 21 20496
4 A AAAAAAAAAA 976 19 18544
5 A AAAAAAAAAA 976 14 13664
6 A AAAAAAAAAA 976 11 10736
7 A AAAAAAAAAA 976 18 17568
8 A AAAAAAAAAA 976 8 7808
9 A AAAAAAAAAA 976 11 10736
10 A AAAAAAAAAA 976 7 6832
11 A AAAAAAAAAA 976 13 12688
12 B BBBBBBBBBB 898 9 8082
13 B BBBBBBBBBB 898 13 11674
14 B BBBBBBBBBB 898 25 22450
15 B BBBBBBBBBB 898 11 9878
16 B BBBBBBBBBB 898 8 7184
17 C CCCCCCCCCC 786 22 17292
18 C CCCCCCCCCC 786 9 7074
19 D DDDDDDDDDD 488 22 10736
20 D DDDDDDDDDD 488 21 10248
21 D DDDDDDDDDD 488 15 7320
22 D DDDDDDDDDD 488 15 7320
23 D DDDDDDDDDD 488 23 11224
24 D DDDDDDDDDD 488 18 8784
25 E EEEEEEEEEE 401 17 6817
26 E EEEEEEEEEE 401 6 2406
27 E EEEEEEEEEE 401 17 6817
28 E EEEEEEEEEE 401 16 6416
29 E EEEEEEEEEE 401 5 2005
30 E EEEEEEEEEE 401 23 9223tibble [5 × 2] (S3: tbl_df/tbl/data.frame)
$ name : chr [1:5] "AAAAAAAAAA" "BBBBBBBBBB" "CCCCCCCCCC" "DDDDDDDDDD" ...
$ tcost: int [1:5] 149328 59268 24366 55632 33684# A tibble: 5 × 2
name tcost
<chr> <int>
1 AAAAAAAAAA 149328
2 BBBBBBBBBB 59268
3 DDDDDDDDDD 55632
4 EEEEEEEEEE 33684
5 CCCCCCCCCC 24366$Aind
integer(0)
$Bind
[1] 1 2 3
$Cind
[1] 4 5
$ABexchanged
[1] TRUE
$A
Effort Yield
0.2500000 0.5222652
$B
Effort Yield
0.3200000 0.5848359
$C
Effort Yield
0.5300000 0.7624388
$smallestAData
[1] 0.5222652
$smallestBData
[1] 0.7624388
$AlimitIndInInterpolation
[1] 26
$BlimitIndInInterpolation
[1] 54
$p
[1] 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10 0.11 0.12 0.13 0.14
[16] 0.15 0.16 0.17 0.18 0.19 0.20 0.21 0.22 0.23 0.24 0.25 0.26 0.27 0.28 0.29
[31] 0.30 0.31 0.32 0.33 0.34 0.35 0.36 0.37 0.38 0.39 0.40 0.41 0.42 0.43 0.44
[46] 0.45 0.46 0.47 0.48 0.49 0.50 0.51 0.52 0.53 0.54 0.55 0.56 0.57 0.58 0.59
[61] 0.60 0.61 0.62 0.63 0.64 0.65 0.66 0.67 0.68 0.69 0.70 0.71 0.72 0.73 0.74
[76] 0.75 0.76 0.77 0.78 0.79 0.80 0.81 0.82 0.83 0.84 0.85 0.86 0.87 0.88 0.89
[91] 0.90 0.91 0.92 0.93 0.94 0.95 0.96 0.97 0.98 0.99 1.00
$ABC
[1] 0.00000000 0.03441467 0.06744432 0.09912245 0.12948260 0.15855828
[7] 0.18638301 0.21299032 0.23841372 0.26268674 0.28584290 0.30791571
[13] 0.32893870 0.34894539 0.36796930 0.38604394 0.40320285 0.41947955
[19] 0.43490754 0.44952036 0.46335152 0.47643702 0.48882276 0.50055711
[25] 0.51168846 0.52226517 0.53233563 0.54194820 0.55115125 0.55999318
[31] 0.56852235 0.57678713 0.58483591 0.59271705 0.60047894 0.60816994
[37] 0.61583844 0.62353280 0.63130141 0.63919264 0.64725485 0.65552406
[43] 0.66398674 0.67261700 0.68138893 0.69027665 0.69925424 0.70829583
[49] 0.71737550 0.72646736 0.73554552 0.74458408 0.75355714 0.76243881
[55] 0.77120318 0.77982436 0.78827645 0.79653356 0.80456979 0.81235923
[61] 0.81987601 0.82710062 0.83403927 0.84070453 0.84710902 0.85326532
[67] 0.85918604 0.86488377 0.87037112 0.87566067 0.88076503 0.88569679
[73] 0.89046856 0.89509292 0.89958248 0.90394984 0.90820759 0.91236833
[79] 0.91644466 0.92044917 0.92439447 0.92829186 0.93214753 0.93596639
[85] 0.93975331 0.94351322 0.94725100 0.95097155 0.95467977 0.95838057
[91] 0.96207883 0.96577946 0.96948736 0.97320743 0.97694455 0.98070364
[97] 0.98448960 0.98830731 0.99216168 0.99605761 1.00000000
$ABLimit
[1] 149328
$BCLimit
[1] 55632
julia setup
loading packages
All packages loaded successfully.
All packages loaded successfully.
All packages loaded successfully.
glfw initialised" "6-element Vector{String}:
"Random"
"DataFrames"
"RDatasets"
"Plots"
"SciMLBase"
"GpABC"All packages loaded successfully.loading packages
3-element Vector{String}:
"Random"
"Plots"
"GR"All packages loaded successfully.-5:1:5my (generic function with 1 method)Plots.GRBackend()
working with dataframes
TaskLocalRNG()6×2 DataFrame
Row │ popat1 popat2
│ Float64 Float64
─────┼─────────────────────
1 │ 0.0491718 0.944318
2 │ 0.119079 0.46105
3 │ 0.393271 0.830334
4 │ 0.0240943 0.573132
5 │ 0.691857 0.176625
6 │ 0.767518 0.1149352×7 DataFrame
Row │ variable mean min median max nmissing eltype
│ Symbol Float64 Float64 Float64 Float64 Int64 DataType
─────┼───────────────────────────────────────────────────────────────────────
1 │ popat1 0.445195 0.0240943 0.527348 0.855718 0 Float64
2 │ popat2 0.524112 0.114935 0.517091 0.944318 0 Float64[0.0491718221481211, 0.11907881640750706, 0.3932710232252806, 0.024094310524527707, 0.6918572875342215, 0.7675180540873912, 0.08725304891274233, 0.8557176841095734, 0.8025607099234905, 0.661425351684768][0.0491718221481211, 0.11907881640750706, 0.3932710232252806, 0.024094310524527707, 0.6918572875342215, 0.7675180540873912, 0.08725304891274233, 0.8557176841095734, 0.8025607099234905, 0.661425351684768]["popat1", "popat2"]10×2 DataFrame
Row │ popat1 popat2
│ Float64 Float64
─────┼─────────────────────
1 │ 0.0491718 0.944318
2 │ 0.119079 0.46105
3 │ 0.393271 0.830334
4 │ 0.0240943 0.573132
5 │ 0.691857 0.176625
6 │ 0.767518 0.114935
7 │ 0.087253 0.7864
8 │ 0.855718 0.892598
9 │ 0.802561 0.207253
10 │ 0.661425 0.25447210×2 DataFrame
Row │ popat1 popat2
│ Float64 Float64
─────┼─────────────────────
1 │ 0.0491718 0.944318
2 │ 0.119079 0.46105
3 │ 0.393271 0.830334
4 │ 0.0240943 0.573132
5 │ 0.691857 0.176625
6 │ 0.767518 0.114935
7 │ 0.087253 0.7864
8 │ 0.855718 0.892598
9 │ 0.802561 0.207253
10 │ 0.661425 0.254472"dtmp10215.csv"trueThe file dtmp10215.csv exists.All packages loaded successfully.10×2 DataFrame
Row │ popat1 popat2
│ Float64 Float64
─────┼─────────────────────
1 │ 0.0491718 0.944318
2 │ 0.119079 0.46105
3 │ 0.393271 0.830334
4 │ 0.0240943 0.573132
5 │ 0.691857 0.176625
6 │ 0.767518 0.114935
7 │ 0.087253 0.7864
8 │ 0.855718 0.892598
9 │ 0.802561 0.207253
10 │ 0.661425 0.254472
solving small linear equation
[1 1; 1 -1][10, 5]2-element Vector{Float64}:
7.5
2.5[7.5, 2.5]solving large linear equation
1-element Vector{String}:
"LinearAlgebra"All packages loaded successfully.4.740436406477113
-2.7048316242181025
8.84445031591571
-12.18760403297759
-10.760104744507274
-0.6370867741423903
2.92433835274419
3.763709603261683
3.188440276898675
18.7617419271199510×1 Matrix{Float64}:
2.220446049250313e-16
-1.6653345369377348e-15
3.4416913763379853e-15
-1.1102230246251565e-16
-1.3877787807814457e-16
-9.992007221626409e-16
1.4432899320127035e-15
-2.220446049250313e-15
2.2620794126737565e-15
-6.661338147750939e-162.220446049250313e-16
-1.6653345369377348e-15
3.4416913763379853e-15
-1.1102230246251565e-16
-1.3877787807814457e-16
-9.992007221626409e-16
1.4432899320127035e-15
-2.220446049250313e-15
2.2620794126737565e-15
-6.661338147750939e-16histogram with frequency & density
3-element Vector{String}:
"Distributions"
"Plots"
"KernelDensity"All packages loaded successfully.Normal{Float64}(μ=100.0, σ=10.0)1000-element Vector{Float64}:
107.14239454177303
112.15416632163475
122.50508395669189
89.69436513203053
87.6361252843894
91.1750826426113
110.72775710668704
90.05507424672686
94.24706740996659
107.40201249783048
⋮
103.02805925456413
124.58899439180635
94.98397866596254
80.01139491277324
82.92420298648162
101.55509941876329
91.3235805424571
95.6705118100269
103.08985851332379
Plots.Series(RecipesPipeline.DefaultsDict(:plot_object => Plot{Plots.GRBackend() n=1}, :subplot => Subplot{1}, :markershape => :none, :label => "y1", :fillalpha => nothing, :orientation => :vertical, :linealpha => nothing, :x_extrema => (NaN, NaN), :arrow => nothing, :series_index => 1…))5.0
Linear Regression
4-element Vector{String}:
"Random"
"DataFrames"
"CSV"
"GLM"All packages loaded successfully.TaskLocalRNG()101:10my (generic function with 2 methods)10-element Vector{Float64}:
8.366831772346464
12.746207447785931
17.49413341845734
20.14132370171251
24.574645018314158
23.964040581229376
29.85090173942833
32.902596318375934
35.35131979172247
35.838560532354634[8.366831772346464, 12.746207447785931, 17.49413341845734, 20.14132370171251, 24.574645018314158, 23.964040581229376, 29.85090173942833, 32.902596318375934, 35.35131979172247, 35.838560532354634]
"C:\\quarto27012025\\tmp.png"10×2 Matrix{Float64}:
1.0 8.36683
2.0 12.7462
3.0 17.4941
4.0 20.1413
5.0 24.5746
6.0 23.964
7.0 29.8509
8.0 32.9026
9.0 35.3513
10.0 35.838610×2 DataFrame
Row │ x y
│ Float64 Float64
─────┼───────────────────
1 │ 1.0 8.36683
2 │ 2.0 12.7462
3 │ 3.0 17.4941
4 │ 4.0 20.1413
5 │ 5.0 24.5746
6 │ 6.0 23.964
7 │ 7.0 29.8509
8 │ 8.0 32.9026
9 │ 9.0 35.3513
10 │ 10.0 35.838610×2 DataFrame
Row │ x y
│ Float64 Float64
─────┼───────────────────
1 │ 1.0 8.36683
2 │ 2.0 12.7462
3 │ 3.0 17.4941
4 │ 4.0 20.1413
5 │ 5.0 24.5746
6 │ 6.0 23.964
7 │ 7.0 29.8509
8 │ 8.0 32.9026
9 │ 9.0 35.3513
10 │ 10.0 35.8386"tmp.csv"StatsModels.TableRegressionModel{LinearModel{GLM.LmResp{Vector{Float64}}, GLM.DensePredChol{Float64, CholeskyPivoted{Float64, Matrix{Float64}, Vector{Int64}}}}, Matrix{Float64}}
y ~ 1 + x
Coefficients:
───────────────────────────────────────────────────────────────────────
Coef. Std. Error t Pr(>|t|) Lower 95% Upper 95%
───────────────────────────────────────────────────────────────────────
(Intercept) 7.08833 1.06729 6.64 0.0002 4.62715 9.54951
x 3.09722 0.17201 18.01 <1e-07 2.70057 3.49388
───────────────────────────────────────────────────────────────────────StatsModels.TableRegressionModel{LinearModel{GLM.LmResp{Vector{Float64}}, GLM.DensePredChol{Float64, CholeskyPivoted{Float64, Matrix{Float64}, Vector{Int64}}}}, Matrix{Float64}}
y ~ 1 + x
Coefficients:
───────────────────────────────────────────────────────────────────────
Coef. Std. Error t Pr(>|t|) Lower 95% Upper 95%
───────────────────────────────────────────────────────────────────────
(Intercept) 7.08833 1.06729 6.64 0.0002 4.62715 9.54951
x 3.09722 0.17201 18.01 <1e-07 2.70057 3.49388
───────────────────────────────────────────────────────────────────────[7.088329718063239, 3.097222966201723]2×1 DataFrame
Row │ x
│ Float64
─────┼─────────
1 │ 5.5
2 │ 9.32-element Vector{Union{Missing, Float64}}:
24.123056032172716
35.89250330373926Union{Missing, Float64}[24.123056032172716, 35.89250330373926]2-element Vector{Union{Missing, Float64}}:
24.123056032172716
35.892503303739262×2 DataFrame
Row │ x py
│ Float64 Float64?
─────┼───────────────────
1 │ 5.5 24.1231
2 │ 9.3 35.8925histogram with normal density curve in julia
hwnc (generic function with 1 method)10010100Normal{Float64}(μ=100.0, σ=10.0)100-element Vector{Float64}:
111.7802591371556
103.85511601627927
104.04152107206976
98.96424017031497
105.95319156684396
104.55470483254561
92.98082482726603
87.3291108433959
98.92129655504161
107.1833151703558
⋮
108.25303693370215
102.46148788764755
110.76246060033249
89.85041368927503
100.07460362971408
112.08620015351264
92.46866102892267
94.02111638978063
98.50938982305627
bhankas with julia
kbv_hwnc (generic function with 1 method)7-element Vector{String}:
"StatsBase"
"Images"
"ImageView"
"Plots"
"MLJ"
"RDatasets"
"MLJModels"All packages loaded successfully.-5:1:5kbv1 (generic function with 1 method)11-element Vector{Int64}:
-208
-115
-52
-13
8
17
20
23
32
53
92Plots.GRBackend()
"C:\\quarto27012025\\jup1.png"TaskLocalRNG()10000501010000-element Vector{Float64}:
50.6193274031408
52.78405814164
44.04175584635948
50.466593895733816
60.85794021543276
34.23435077414016
51.759399913010746
58.653808054093254
22.09718994450693
31.079844417740873
⋮
52.413115928596625
49.025474847116975
76.3170646324677
59.048108777995076
48.00603105315709
43.491790609401015
48.29383789460438
48.4884742176607
36.51482096334351Julia variable importance
method -1
5-element Vector{String}:
"RDatasets"
"FeatureSelectors"
"DataFrames"
"CategoricalArrays"
"CSV"All packages loaded successfully.UnivariateFeatureSelector(FeatureSelectors.pearson_correlation, 5, nothing)5-element Vector{String}:
"LStat"
"Rm"
"PTRatio"
"Indus"
"Tax"5×7 DataFrame
Row │ variable mean min median max nmissing eltype
│ Symbol Union… Any Union… Any Int64 DataType
─────┼────────────────────────────────────────────────────────────────────────────────────────────
1 │ SepalLength 5.84333 4.3 5.8 7.9 0 Float64
2 │ SepalWidth 3.05733 2.0 3.0 4.4 0 Float64
3 │ PetalLength 3.758 1.0 4.35 6.9 0 Float64
4 │ PetalWidth 1.19933 0.1 1.3 2.5 0 Float64
5 │ Species setosa virginica 0 CategoricalValue{String, UInt8}UnivariateFeatureSelector(FeatureSelectors.pearson_correlation, 4, nothing)4-element Vector{String}:
"PetalWidth"
"PetalLength"
"SepalLength"
"SepalWidth"["PetalWidth", "PetalLength", "SepalLength", "SepalWidth"]method-2
8-element Vector{String}:
"RDatasets"
"MLJ"
"MLJBase"
"MLJModels"
"DataFrames"
"MLJDecisionTreeInterface"
"Random"
"DecisionTree"All packages loaded successfully.150×5 DataFrame
Row │ SepalLength SepalWidth PetalLength PetalWidth Species
│ Float64 Float64 Float64 Float64 Cat…
─────┼─────────────────────────────────────────────────────────────
1 │ 5.1 3.5 1.4 0.2 setosa
2 │ 4.9 3.0 1.4 0.2 setosa
3 │ 4.7 3.2 1.3 0.2 setosa
4 │ 4.6 3.1 1.5 0.2 setosa
5 │ 5.0 3.6 1.4 0.2 setosa
6 │ 5.4 3.9 1.7 0.4 setosa
7 │ 4.6 3.4 1.4 0.3 setosa
8 │ 5.0 3.4 1.5 0.2 setosa
⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮
144 │ 6.8 3.2 5.9 2.3 virginica
145 │ 6.7 3.3 5.7 2.5 virginica
146 │ 6.7 3.0 5.2 2.3 virginica
147 │ 6.3 2.5 5.0 1.9 virginica
148 │ 6.5 3.0 5.2 2.0 virginica
149 │ 6.2 3.4 5.4 2.3 virginica
150 │ 5.9 3.0 5.1 1.8 virginica
135 rows omitted150×4 DataFrame
Row │ SepalLength SepalWidth PetalLength PetalWidth
│ Float64 Float64 Float64 Float64
─────┼──────────────────────────────────────────────────
1 │ 5.1 3.5 1.4 0.2
2 │ 4.9 3.0 1.4 0.2
3 │ 4.7 3.2 1.3 0.2
4 │ 4.6 3.1 1.5 0.2
5 │ 5.0 3.6 1.4 0.2
6 │ 5.4 3.9 1.7 0.4
7 │ 4.6 3.4 1.4 0.3
8 │ 5.0 3.4 1.5 0.2
⋮ │ ⋮ ⋮ ⋮ ⋮
144 │ 6.8 3.2 5.9 2.3
145 │ 6.7 3.3 5.7 2.5
146 │ 6.7 3.0 5.2 2.3
147 │ 6.3 2.5 5.0 1.9
148 │ 6.5 3.0 5.2 2.0
149 │ 6.2 3.4 5.4 2.3
150 │ 5.9 3.0 5.1 1.8
135 rows omitted150-element CategoricalArray{String,1,UInt8}:
"setosa"
"setosa"
"setosa"
"setosa"
"setosa"
"setosa"
"setosa"
"setosa"
"setosa"
"setosa"
⋮
"virginica"
"virginica"
"virginica"
"virginica"
"virginica"
"virginica"
"virginica"
"virginica"
"virginica"TaskLocalRNG()RandomForestClassifier(
max_depth = -1,
min_samples_leaf = 1,
min_samples_split = 2,
min_purity_increase = 0.0,
n_subfeatures = -1,
n_trees = 100,
sampling_fraction = 0.7,
feature_importance = :impurity,
rng = TaskLocalRNG())untrained Machine; caches model-specific representations of data
model: RandomForestClassifier(max_depth = -1, …)
args:
1: Source @507 ⏎ Table{AbstractVector{ScientificTypesBase.Continuous}}
2: Source @608 ⏎ AbstractVector{Multiclass{3}}trained Machine; caches model-specific representations of data
model: RandomForestClassifier(max_depth = -1, …)
args:
1: Source @507 ⏎ Table{AbstractVector{ScientificTypesBase.Continuous}}
2: Source @608 ⏎ AbstractVector{Multiclass{3}}Vector{Pair{Symbol, Float64}} (alias for Array{Pair{Symbol, Float64}, 1})Feature Importances::PetalWidth => 0.45098651744544205
:PetalLength => 0.42261825648864204
:SepalLength => 0.10388331996352827
:SepalWidth => 0.022511906102387645test julia
5.6---60.16data visualisation with julia
6-element Vector{String}:
"RDatasets"
"Plots"
"DataFrames"
"Colors"
"Images"
"ImageView"All packages loaded successfully.150×5 DataFrame
Row │ SepalLength SepalWidth PetalLength PetalWidth Species
│ Float64 Float64 Float64 Float64 Cat…
─────┼─────────────────────────────────────────────────────────────
1 │ 5.1 3.5 1.4 0.2 setosa
2 │ 4.9 3.0 1.4 0.2 setosa
3 │ 4.7 3.2 1.3 0.2 setosa
4 │ 4.6 3.1 1.5 0.2 setosa
5 │ 5.0 3.6 1.4 0.2 setosa
6 │ 5.4 3.9 1.7 0.4 setosa
7 │ 4.6 3.4 1.4 0.3 setosa
8 │ 5.0 3.4 1.5 0.2 setosa
⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮
144 │ 6.8 3.2 5.9 2.3 virginica
145 │ 6.7 3.3 5.7 2.5 virginica
146 │ 6.7 3.0 5.2 2.3 virginica
147 │ 6.3 2.5 5.0 1.9 virginica
148 │ 6.5 3.0 5.2 2.0 virginica
149 │ 6.2 3.4 5.4 2.3 virginica
150 │ 5.9 3.0 5.1 1.8 virginica
135 rows omittedPlots.GRBackend()"C:\\quarto27012025\\q1.png"
3×2 DataFrame
Row │ Species Count
│ Cat… Int64
─────┼───────────────────
1 │ setosa 50
2 │ versicolor 50
3 │ virginica 503-element Vector{Float64}:
33.333333333333336
33.333333333333336
33.3333333333333363×3 DataFrame
Row │ Species Count pct
│ Cat… Int64 Float64
─────┼────────────────────────────
1 │ setosa 50 33.3333
2 │ versicolor 50 33.3333
3 │ virginica 50 33.3333my_round (generic function with 2 methods)3-element CategoricalArray{String,1,UInt8}:
"setosa"
"versicolor"
"virginica"3-element Vector{Int64}:
50
50
503-element Vector{Float64}:
33.333333333333336
33.333333333333336
33.3333333333333363-element Vector{Float64}:
33.33
33.33
33.33
3-element Vector{Float64}:
33.333333333333336
33.333333333333336
33.3333333333333363-element Vector{Float64}:
33.33
33.33
33.333-element Vector{Float64}:
0.0
0.0
0.03-element Vector{Float64}:
0.0
0.0
0.0
3-element Vector{Float64}:
60.0
180.00000000000003
300.03-element Vector{Tuple{Float64, Float64}}:
(0.8660254037844386, 0.5)
(-4.960524086056721e-16, -1.0)
(-0.8660254037844386, 0.5)zip([33.33, 33.33, 33.33], [(0.8660254037844386, 0.5), (-4.960524086056721e-16, -1.0), (-0.8660254037844386, 0.5)])
lp with julia
A JuMP Model
├ solver: GLPK
├ objective_sense: FEASIBILITY_SENSE
├ num_variables: 0
├ num_constraints: 0
└ Names registered in the model: nonexy12 x + 20 yx + y <= 1002 x + 3 y <= 120y <= 30Max 12 x + 20 y
Subject to
x + y <= 100
2 x + 3 y <= 120
y <= 30
x >= 0
y >= 0Optimal solution:objective value is 780.0x = 15.0y = 30.0rndom forest dt mining with julia
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3][1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 3, 2, 2, 2, 2, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3][0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, -1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, -1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
0.9333333333333333data mining using MLJ julia Package
All packages loaded successfully.
########################
difeerent models in mlj
########################
231×3 DataFrame
Row │ name package_name is_supervised
│ String String Bool
─────┼────────────────────────────────────────────────────────────────────────────────
1 │ ABODDetector OutlierDetectionNeighbors false
2 │ ABODDetector OutlierDetectionPython false
3 │ ARDRegressor MLJScikitLearnInterface true
4 │ AdaBoostClassifier MLJScikitLearnInterface true
5 │ AdaBoostRegressor MLJScikitLearnInterface true
6 │ AdaBoostStumpClassifier DecisionTree true
7 │ AffinityPropagation MLJScikitLearnInterface false
8 │ AgglomerativeClustering MLJScikitLearnInterface false
9 │ AutoEncoder BetaML false
10 │ BM25Transformer MLJText false
11 │ BaggingClassifier MLJScikitLearnInterface true
12 │ BaggingRegressor MLJScikitLearnInterface true
13 │ BayesianLDA MLJScikitLearnInterface true
14 │ BayesianLDA MultivariateStats true
15 │ BayesianQDA MLJScikitLearnInterface true
16 │ BayesianRidgeRegressor MLJScikitLearnInterface true
17 │ BayesianSubspaceLDA MultivariateStats true
18 │ BernoulliNBClassifier MLJScikitLearnInterface true
19 │ Birch MLJScikitLearnInterface false
20 │ BisectingKMeans MLJScikitLearnInterface false
21 │ BorderlineSMOTE1 Imbalance false
22 │ CBLOFDetector OutlierDetectionPython false
23 │ CDDetector OutlierDetectionPython false
24 │ COFDetector OutlierDetectionNeighbors false
25 │ COFDetector OutlierDetectionPython false
26 │ COPODDetector OutlierDetectionPython false
27 │ CatBoostClassifier CatBoost true
28 │ CatBoostRegressor CatBoost true
29 │ ClusterUndersampler Imbalance false
30 │ ComplementNBClassifier MLJScikitLearnInterface true
31 │ ConstantClassifier MLJModels true
32 │ ConstantRegressor MLJModels true
33 │ ContinuousEncoder MLJModels false
34 │ CountTransformer MLJText false
35 │ DBSCAN Clustering false
36 │ DBSCAN MLJScikitLearnInterface false
37 │ DNNDetector OutlierDetectionNeighbors false
38 │ DecisionTreeClassifier BetaML true
39 │ DecisionTreeClassifier DecisionTree true
40 │ DecisionTreeRegressor BetaML true
41 │ DecisionTreeRegressor DecisionTree true
42 │ DeterministicConstantClassifier MLJModels true
43 │ DeterministicConstantRegressor MLJModels true
44 │ DummyClassifier MLJScikitLearnInterface true
45 │ DummyRegressor MLJScikitLearnInterface true
46 │ ECODDetector OutlierDetectionPython false
47 │ ENNUndersampler Imbalance false
48 │ ElasticNetCVRegressor MLJScikitLearnInterface true
49 │ ElasticNetRegressor MLJLinearModels true
50 │ ElasticNetRegressor MLJScikitLearnInterface true
51 │ EpsilonSVR LIBSVM true
52 │ EvoLinearRegressor EvoLinear true
53 │ EvoSplineRegressor EvoLinear true
54 │ EvoTreeClassifier EvoTrees true
55 │ EvoTreeCount EvoTrees true
56 │ EvoTreeGaussian EvoTrees true
57 │ EvoTreeMLE EvoTrees true
58 │ EvoTreeRegressor EvoTrees true
59 │ ExtraTreesClassifier MLJScikitLearnInterface true
60 │ ExtraTreesRegressor MLJScikitLearnInterface true
61 │ FactorAnalysis MultivariateStats false
62 │ FeatureAgglomeration MLJScikitLearnInterface false
63 │ FeatureSelector MLJModels false
64 │ FillImputer MLJModels false
65 │ GMMDetector OutlierDetectionPython false
66 │ GaussianMixtureClusterer BetaML false
67 │ GaussianMixtureImputer BetaML false
68 │ GaussianMixtureRegressor BetaML true
69 │ GaussianNBClassifier MLJScikitLearnInterface true
70 │ GaussianNBClassifier NaiveBayes true
71 │ GaussianProcessClassifier MLJScikitLearnInterface true
72 │ GaussianProcessRegressor MLJScikitLearnInterface true
73 │ GeneralImputer BetaML false
74 │ GradientBoostingClassifier MLJScikitLearnInterface true
75 │ GradientBoostingRegressor MLJScikitLearnInterface true
76 │ HBOSDetector OutlierDetectionPython false
77 │ HDBSCAN MLJScikitLearnInterface false
78 │ HierarchicalClustering Clustering false
79 │ HistGradientBoostingClassifier MLJScikitLearnInterface true
80 │ HistGradientBoostingRegressor MLJScikitLearnInterface true
81 │ HuberRegressor MLJLinearModels true
82 │ HuberRegressor MLJScikitLearnInterface true
83 │ ICA MultivariateStats false
84 │ IForestDetector OutlierDetectionPython false
85 │ INNEDetector OutlierDetectionPython false
86 │ ImageClassifier MLJFlux true
87 │ InteractionTransformer MLJModels false
88 │ KDEDetector OutlierDetectionPython false
89 │ KMeans Clustering false
90 │ KMeans MLJScikitLearnInterface false
91 │ KMeans ParallelKMeans false
92 │ KMeansClusterer BetaML false
93 │ KMedoids Clustering false
94 │ KMedoidsClusterer BetaML false
95 │ KNNClassifier NearestNeighborModels true
96 │ KNNDetector OutlierDetectionNeighbors false
97 │ KNNDetector OutlierDetectionPython false
98 │ KNNRegressor NearestNeighborModels true
99 │ KNeighborsClassifier MLJScikitLearnInterface true
100 │ KNeighborsRegressor MLJScikitLearnInterface true
101 │ KPLSRegressor PartialLeastSquaresRegressor true
102 │ KernelPCA MultivariateStats false
103 │ KernelPerceptronClassifier BetaML true
104 │ LADRegressor MLJLinearModels true
105 │ LDA MultivariateStats true
106 │ LGBMClassifier LightGBM true
107 │ LGBMRegressor LightGBM true
108 │ LMDDDetector OutlierDetectionPython false
109 │ LOCIDetector OutlierDetectionPython false
110 │ LODADetector OutlierDetectionPython false
111 │ LOFDetector OutlierDetectionNeighbors false
112 │ LOFDetector OutlierDetectionPython false
113 │ LarsCVRegressor MLJScikitLearnInterface true
114 │ LarsRegressor MLJScikitLearnInterface true
115 │ LassoCVRegressor MLJScikitLearnInterface true
116 │ LassoLarsCVRegressor MLJScikitLearnInterface true
117 │ LassoLarsICRegressor MLJScikitLearnInterface true
118 │ LassoLarsRegressor MLJScikitLearnInterface true
119 │ LassoRegressor MLJLinearModels true
120 │ LassoRegressor MLJScikitLearnInterface true
121 │ LinearBinaryClassifier GLM true
122 │ LinearCountRegressor GLM true
123 │ LinearRegressor GLM true
124 │ LinearRegressor MLJLinearModels true
125 │ LinearRegressor MLJScikitLearnInterface true
126 │ LinearRegressor MultivariateStats true
127 │ LinearSVC LIBSVM true
128 │ LogisticCVClassifier MLJScikitLearnInterface true
129 │ LogisticClassifier MLJLinearModels true
130 │ LogisticClassifier MLJScikitLearnInterface true
131 │ MCDDetector OutlierDetectionPython false
132 │ MeanShift MLJScikitLearnInterface false
133 │ MiniBatchKMeans MLJScikitLearnInterface false
134 │ MultiTaskElasticNetCVRegressor MLJScikitLearnInterface true
135 │ MultiTaskElasticNetRegressor MLJScikitLearnInterface true
136 │ MultiTaskLassoCVRegressor MLJScikitLearnInterface true
137 │ MultiTaskLassoRegressor MLJScikitLearnInterface true
138 │ MultinomialClassifier MLJLinearModels true
139 │ MultinomialNBClassifier MLJScikitLearnInterface true
140 │ MultinomialNBClassifier NaiveBayes true
141 │ MultitargetGaussianMixtureRegres… BetaML true
142 │ MultitargetKNNClassifier NearestNeighborModels true
143 │ MultitargetKNNRegressor NearestNeighborModels true
144 │ MultitargetLinearRegressor MultivariateStats true
145 │ MultitargetNeuralNetworkRegressor BetaML true
146 │ MultitargetNeuralNetworkRegressor MLJFlux true
147 │ MultitargetRidgeRegressor MultivariateStats true
148 │ MultitargetSRRegressor SymbolicRegression true
149 │ NeuralNetworkClassifier BetaML true
150 │ NeuralNetworkClassifier MLJFlux true
151 │ NeuralNetworkRegressor BetaML true
152 │ NeuralNetworkRegressor MLJFlux true
153 │ NuSVC LIBSVM true
154 │ NuSVR LIBSVM true
155 │ OCSVMDetector OutlierDetectionPython false
156 │ OPTICS MLJScikitLearnInterface false
157 │ OneClassSVM LIBSVM false
158 │ OneHotEncoder MLJModels false
159 │ OneRuleClassifier OneRule true
160 │ OrthogonalMatchingPursuitCVRegre… MLJScikitLearnInterface true
161 │ OrthogonalMatchingPursuitRegress… MLJScikitLearnInterface true
162 │ PCA MultivariateStats false
163 │ PCADetector OutlierDetectionPython false
164 │ PLSRegressor PartialLeastSquaresRegressor true
165 │ PPCA MultivariateStats false
166 │ PartLS PartitionedLS true
167 │ PassiveAggressiveClassifier MLJScikitLearnInterface true
168 │ PassiveAggressiveRegressor MLJScikitLearnInterface true
169 │ PegasosClassifier BetaML true
170 │ PerceptronClassifier BetaML true
171 │ PerceptronClassifier MLJScikitLearnInterface true
172 │ ProbabilisticNuSVC LIBSVM true
173 │ ProbabilisticSGDClassifier MLJScikitLearnInterface true
174 │ ProbabilisticSVC LIBSVM true
175 │ QuantileRegressor MLJLinearModels true
176 │ RANSACRegressor MLJScikitLearnInterface true
177 │ RODDetector OutlierDetectionPython false
178 │ ROSE Imbalance false
179 │ RandomForestClassifier BetaML true
180 │ RandomForestClassifier DecisionTree true
181 │ RandomForestClassifier MLJScikitLearnInterface true
182 │ RandomForestImputer BetaML false
183 │ RandomForestRegressor BetaML true
184 │ RandomForestRegressor DecisionTree true
185 │ RandomForestRegressor MLJScikitLearnInterface true
186 │ RandomOversampler Imbalance false
187 │ RandomUndersampler Imbalance false
188 │ RandomWalkOversampler Imbalance false
189 │ RidgeCVClassifier MLJScikitLearnInterface true
190 │ RidgeCVRegressor MLJScikitLearnInterface true
191 │ RidgeClassifier MLJScikitLearnInterface true
192 │ RidgeRegressor MLJLinearModels true
193 │ RidgeRegressor MLJScikitLearnInterface true
194 │ RidgeRegressor MultivariateStats true
195 │ RobustRegressor MLJLinearModels true
196 │ SGDClassifier MLJScikitLearnInterface true
197 │ SGDRegressor MLJScikitLearnInterface true
198 │ SMOTE Imbalance false
199 │ SMOTEN Imbalance false
200 │ SMOTENC Imbalance false
201 │ SODDetector OutlierDetectionPython false
202 │ SOSDetector OutlierDetectionPython false
203 │ SRRegressor SymbolicRegression true
204 │ SVC LIBSVM true
205 │ SVMClassifier MLJScikitLearnInterface true
206 │ SVMLinearClassifier MLJScikitLearnInterface true
207 │ SVMLinearRegressor MLJScikitLearnInterface true
208 │ SVMNuClassifier MLJScikitLearnInterface true
209 │ SVMNuRegressor MLJScikitLearnInterface true
210 │ SVMRegressor MLJScikitLearnInterface true
211 │ SelfOrganizingMap SelfOrganizingMaps false
212 │ SimpleImputer BetaML false
213 │ SpectralClustering MLJScikitLearnInterface false
214 │ StableForestClassifier SIRUS true
215 │ StableForestRegressor SIRUS true
216 │ StableRulesClassifier SIRUS true
217 │ StableRulesRegressor SIRUS true
218 │ Standardizer MLJModels false
219 │ SubspaceLDA MultivariateStats true
220 │ TSVDTransformer TSVD false
221 │ TfidfTransformer MLJText false
222 │ TheilSenRegressor MLJScikitLearnInterface true
223 │ TomekUndersampler Imbalance false
224 │ UnivariateBoxCoxTransformer MLJModels false
225 │ UnivariateDiscretizer MLJModels false
226 │ UnivariateFillImputer MLJModels false
227 │ UnivariateStandardizer MLJModels false
228 │ UnivariateTimeTypeToContinuous MLJModels false
229 │ XGBoostClassifier XGBoost true
230 │ XGBoostCount XGBoost true
231 │ XGBoostRegressor XGBoost true
146×3 DataFrame
Row │ name package_name is_supervised
│ String String Bool
─────┼────────────────────────────────────────────────────────────────────────────────
1 │ DecisionTreeClassifier BetaML true
2 │ DecisionTreeRegressor BetaML true
3 │ GaussianMixtureRegressor BetaML true
4 │ KernelPerceptronClassifier BetaML true
5 │ MultitargetGaussianMixtureRegres… BetaML true
6 │ MultitargetNeuralNetworkRegressor BetaML true
7 │ NeuralNetworkClassifier BetaML true
8 │ NeuralNetworkRegressor BetaML true
9 │ PegasosClassifier BetaML true
10 │ PerceptronClassifier BetaML true
11 │ RandomForestClassifier BetaML true
12 │ RandomForestRegressor BetaML true
13 │ CatBoostClassifier CatBoost true
14 │ CatBoostRegressor CatBoost true
15 │ AdaBoostStumpClassifier DecisionTree true
16 │ DecisionTreeClassifier DecisionTree true
17 │ DecisionTreeRegressor DecisionTree true
18 │ RandomForestClassifier DecisionTree true
19 │ RandomForestRegressor DecisionTree true
20 │ EvoLinearRegressor EvoLinear true
21 │ EvoSplineRegressor EvoLinear true
22 │ EvoTreeClassifier EvoTrees true
23 │ EvoTreeCount EvoTrees true
24 │ EvoTreeGaussian EvoTrees true
25 │ EvoTreeMLE EvoTrees true
26 │ EvoTreeRegressor EvoTrees true
27 │ LinearBinaryClassifier GLM true
28 │ LinearCountRegressor GLM true
29 │ LinearRegressor GLM true
30 │ EpsilonSVR LIBSVM true
31 │ LinearSVC LIBSVM true
32 │ NuSVC LIBSVM true
33 │ NuSVR LIBSVM true
34 │ ProbabilisticNuSVC LIBSVM true
35 │ ProbabilisticSVC LIBSVM true
36 │ SVC LIBSVM true
37 │ LGBMClassifier LightGBM true
38 │ LGBMRegressor LightGBM true
39 │ ImageClassifier MLJFlux true
40 │ MultitargetNeuralNetworkRegressor MLJFlux true
41 │ NeuralNetworkClassifier MLJFlux true
42 │ NeuralNetworkRegressor MLJFlux true
43 │ ElasticNetRegressor MLJLinearModels true
44 │ HuberRegressor MLJLinearModels true
45 │ LADRegressor MLJLinearModels true
46 │ LassoRegressor MLJLinearModels true
47 │ LinearRegressor MLJLinearModels true
48 │ LogisticClassifier MLJLinearModels true
49 │ MultinomialClassifier MLJLinearModels true
50 │ QuantileRegressor MLJLinearModels true
51 │ RidgeRegressor MLJLinearModels true
52 │ RobustRegressor MLJLinearModels true
53 │ ConstantClassifier MLJModels true
54 │ ConstantRegressor MLJModels true
55 │ DeterministicConstantClassifier MLJModels true
56 │ DeterministicConstantRegressor MLJModels true
57 │ ARDRegressor MLJScikitLearnInterface true
58 │ AdaBoostClassifier MLJScikitLearnInterface true
59 │ AdaBoostRegressor MLJScikitLearnInterface true
60 │ BaggingClassifier MLJScikitLearnInterface true
61 │ BaggingRegressor MLJScikitLearnInterface true
62 │ BayesianLDA MLJScikitLearnInterface true
63 │ BayesianQDA MLJScikitLearnInterface true
64 │ BayesianRidgeRegressor MLJScikitLearnInterface true
65 │ BernoulliNBClassifier MLJScikitLearnInterface true
66 │ ComplementNBClassifier MLJScikitLearnInterface true
67 │ DummyClassifier MLJScikitLearnInterface true
68 │ DummyRegressor MLJScikitLearnInterface true
69 │ ElasticNetCVRegressor MLJScikitLearnInterface true
70 │ ElasticNetRegressor MLJScikitLearnInterface true
71 │ ExtraTreesClassifier MLJScikitLearnInterface true
72 │ ExtraTreesRegressor MLJScikitLearnInterface true
73 │ GaussianNBClassifier MLJScikitLearnInterface true
74 │ GaussianProcessClassifier MLJScikitLearnInterface true
75 │ GaussianProcessRegressor MLJScikitLearnInterface true
76 │ GradientBoostingClassifier MLJScikitLearnInterface true
77 │ GradientBoostingRegressor MLJScikitLearnInterface true
78 │ HistGradientBoostingClassifier MLJScikitLearnInterface true
79 │ HistGradientBoostingRegressor MLJScikitLearnInterface true
80 │ HuberRegressor MLJScikitLearnInterface true
81 │ KNeighborsClassifier MLJScikitLearnInterface true
82 │ KNeighborsRegressor MLJScikitLearnInterface true
83 │ LarsCVRegressor MLJScikitLearnInterface true
84 │ LarsRegressor MLJScikitLearnInterface true
85 │ LassoCVRegressor MLJScikitLearnInterface true
86 │ LassoLarsCVRegressor MLJScikitLearnInterface true
87 │ LassoLarsICRegressor MLJScikitLearnInterface true
88 │ LassoLarsRegressor MLJScikitLearnInterface true
89 │ LassoRegressor MLJScikitLearnInterface true
90 │ LinearRegressor MLJScikitLearnInterface true
91 │ LogisticCVClassifier MLJScikitLearnInterface true
92 │ LogisticClassifier MLJScikitLearnInterface true
93 │ MultiTaskElasticNetCVRegressor MLJScikitLearnInterface true
94 │ MultiTaskElasticNetRegressor MLJScikitLearnInterface true
95 │ MultiTaskLassoCVRegressor MLJScikitLearnInterface true
96 │ MultiTaskLassoRegressor MLJScikitLearnInterface true
97 │ MultinomialNBClassifier MLJScikitLearnInterface true
98 │ OrthogonalMatchingPursuitCVRegre… MLJScikitLearnInterface true
99 │ OrthogonalMatchingPursuitRegress… MLJScikitLearnInterface true
100 │ PassiveAggressiveClassifier MLJScikitLearnInterface true
101 │ PassiveAggressiveRegressor MLJScikitLearnInterface true
102 │ PerceptronClassifier MLJScikitLearnInterface true
103 │ ProbabilisticSGDClassifier MLJScikitLearnInterface true
104 │ RANSACRegressor MLJScikitLearnInterface true
105 │ RandomForestClassifier MLJScikitLearnInterface true
106 │ RandomForestRegressor MLJScikitLearnInterface true
107 │ RidgeCVClassifier MLJScikitLearnInterface true
108 │ RidgeCVRegressor MLJScikitLearnInterface true
109 │ RidgeClassifier MLJScikitLearnInterface true
110 │ RidgeRegressor MLJScikitLearnInterface true
111 │ SGDClassifier MLJScikitLearnInterface true
112 │ SGDRegressor MLJScikitLearnInterface true
113 │ SVMClassifier MLJScikitLearnInterface true
114 │ SVMLinearClassifier MLJScikitLearnInterface true
115 │ SVMLinearRegressor MLJScikitLearnInterface true
116 │ SVMNuClassifier MLJScikitLearnInterface true
117 │ SVMNuRegressor MLJScikitLearnInterface true
118 │ SVMRegressor MLJScikitLearnInterface true
119 │ TheilSenRegressor MLJScikitLearnInterface true
120 │ BayesianLDA MultivariateStats true
121 │ BayesianSubspaceLDA MultivariateStats true
122 │ LDA MultivariateStats true
123 │ LinearRegressor MultivariateStats true
124 │ MultitargetLinearRegressor MultivariateStats true
125 │ MultitargetRidgeRegressor MultivariateStats true
126 │ RidgeRegressor MultivariateStats true
127 │ SubspaceLDA MultivariateStats true
128 │ GaussianNBClassifier NaiveBayes true
129 │ MultinomialNBClassifier NaiveBayes true
130 │ KNNClassifier NearestNeighborModels true
131 │ KNNRegressor NearestNeighborModels true
132 │ MultitargetKNNClassifier NearestNeighborModels true
133 │ MultitargetKNNRegressor NearestNeighborModels true
134 │ OneRuleClassifier OneRule true
135 │ KPLSRegressor PartialLeastSquaresRegressor true
136 │ PLSRegressor PartialLeastSquaresRegressor true
137 │ PartLS PartitionedLS true
138 │ StableForestClassifier SIRUS true
139 │ StableForestRegressor SIRUS true
140 │ StableRulesClassifier SIRUS true
141 │ StableRulesRegressor SIRUS true
142 │ MultitargetSRRegressor SymbolicRegression true
143 │ SRRegressor SymbolicRegression true
144 │ XGBoostClassifier XGBoost true
145 │ XGBoostCount XGBoost true
146 │ XGBoostRegressor XGBoost true
####################################
difeerent classifiers models in mlj
####################################
1---AdaBoostClassifier---MLJScikitLearnInterface
2---AdaBoostStumpClassifier---DecisionTree
3---BaggingClassifier---MLJScikitLearnInterface
4---BayesianLDA---MLJScikitLearnInterface
5---BayesianLDA---MultivariateStats
6---BayesianQDA---MLJScikitLearnInterface
7---BayesianSubspaceLDA---MultivariateStats
8---CatBoostClassifier---CatBoost
9---ConstantClassifier---MLJModels
10---DecisionTreeClassifier---BetaML
11---DecisionTreeClassifier---DecisionTree
12---DeterministicConstantClassifier---MLJModels
13---DummyClassifier---MLJScikitLearnInterface
14---EvoTreeClassifier---EvoTrees
15---ExtraTreesClassifier---MLJScikitLearnInterface
16---GaussianNBClassifier---MLJScikitLearnInterface
17---GaussianNBClassifier---NaiveBayes
18---GaussianProcessClassifier---MLJScikitLearnInterface
19---GradientBoostingClassifier---MLJScikitLearnInterface
20---HistGradientBoostingClassifier---MLJScikitLearnInterface
21---KNNClassifier---NearestNeighborModels
22---KNeighborsClassifier---MLJScikitLearnInterface
23---KernelPerceptronClassifier---BetaML
24---LDA---MultivariateStats
25---LGBMClassifier---LightGBM
26---LinearSVC---LIBSVM
27---LogisticCVClassifier---MLJScikitLearnInterface
28---LogisticClassifier---MLJLinearModels
29---LogisticClassifier---MLJScikitLearnInterface
30---MultinomialClassifier---MLJLinearModels
31---NeuralNetworkClassifier---BetaML
32---NeuralNetworkClassifier---MLJFlux
33---NuSVC---LIBSVM
34---PassiveAggressiveClassifier---MLJScikitLearnInterface
35---PegasosClassifier---BetaML
36---PerceptronClassifier---BetaML
37---PerceptronClassifier---MLJScikitLearnInterface
38---ProbabilisticNuSVC---LIBSVM
39---ProbabilisticSGDClassifier---MLJScikitLearnInterface
40---ProbabilisticSVC---LIBSVM
41---RandomForestClassifier---BetaML
42---RandomForestClassifier---DecisionTree
43---RandomForestClassifier---MLJScikitLearnInterface
44---RidgeCVClassifier---MLJScikitLearnInterface
45---RidgeClassifier---MLJScikitLearnInterface
46---SGDClassifier---MLJScikitLearnInterface
47---SVC---LIBSVM
48---SVMClassifier---MLJScikitLearnInterface
49---SVMLinearClassifier---MLJScikitLearnInterface
50---SVMNuClassifier---MLJScikitLearnInterface
51---StableForestClassifier---SIRUS
52---StableRulesClassifier---SIRUS
53---SubspaceLDA---MultivariateStats
54---XGBoostClassifier---XGBoost
############
mlj1 running
############
#############################
mlj deecision tree classifier
#############################
start mlj deecision tree classifier
Accuracy: 0.94
ConfusionMatrix{3}([14 0 0; 0 17 2; 0 1 16])
[14 0 0; 0 17 2; 0 1 16]
###########
mlj2running
###########
#################################
starting random forest classifier
#################################
Accuracy: 0.94
ConfusionMatrix{3}([14 0 0; 0 17 2; 0 1 16])
[14 0 0; 0 17 2; 0 1 16]
############
mlj3 running
############
######################
mlj xgboost classifier
######################
Accuracy: 0.96
ConfusionMatrix{3}([14 0 0; 0 17 1; 0 1 17])
[14 0 0; 0 17 1; 0 1 17]
############
mlj4 running
############
#######################
mlj adaboost classifier
#######################
Accuracy: 0.94
ConfusionMatrix{3}([14 0 0; 0 17 2; 0 1 16])
[14 0 0; 0 17 2; 0 1 16]
############
ml51 running
############
#############################
mlj adaboost stump classifier
#############################
Accuracy: 0.96
ConfusionMatrix{3}([14 0 0; 0 17 1; 0 1 17])
[14 0 0; 0 17 1; 0 1 17]
############
mlj6 running
############
############################
mlj NuSVC libsvm classifier
############################
Accuracy: 0.96
ConfusionMatrix{3}([14 0 0; 0 18 2; 0 0 16])
[14 0 0; 0 18 2; 0 0 16]
############
mlj7 running
############
##############################
mlj Neural network classifier
##############################
***
*** Training for 200 epochs with algorithm ADAM.
Training.. avg loss on epoch 1 (1): 1.8870645952963137
Training of 200 epoch completed. Final epoch error: 2.1567533608672576.
Accuracy: 0.28
ConfusionMatrix{3}([14 18 18; 0 0 0; 0 0 0])
[14 18 18; 0 0 0; 0 0 0]
############
mlj8 running
############
######################################################
mlj random forest classifer from decision tree package
######################################################
machine(RandomForestClassifier(max_depth = -1, …), …)
Accuracy: 0.94
ConfusionMatrix{3}([14 0 0; 0 17 2; 0 1 16])
[14 0 0; 0 17 2; 0 1 16]
############
mlj9 running
############
###############################
KNeighborsClassifier Classifier
###############################
Accuracy: 0.98
ConfusionMatrix{3}([14 0 0; 0 18 1; 0 0 17])
[14 0 0; 0 18 1; 0 0 17]
#############
mlj10 running
#############
################################
using SVC classifier from libsvm
################################
Accuracy: 0.94
ConfusionMatrix{3}([14 0 0; 0 18 3; 0 0 15])
[14 0 0; 0 18 3; 0 0 15]
#############
mlj11 running
#############
#########################
using Catboost classifier
#########################
Accuracy: 0.96
ConfusionMatrix{3}([14 0 0; 0 17 1; 0 1 17])
[14 0 0; 0 17 1; 0 1 17]
#############
mlj12 running
#############
#######################
using PegasosClassifier
#######################
Accuracy: 0.96
ConfusionMatrix{3}([14 0 0; 0 17 1; 0 1 17])
[14 0 0; 0 17 1; 0 1 17]
#############
mlj13 running
#############
All packages loaded successfully.
All packages loaded successfully.
All packages loaded successfully.
[1.0, 0.9666666666666667, 0.8666666666666667, 0.9333333333333333, 0.9]
0.9333333333333333---0.05270462766947298
#############
mlj14 running
#############
All packages loaded successfully.
[0.9666666666666667, 0.9666666666666667, 1.0, 0.9333333333333333, 0.8666666666666667]
0.9466666666666669----0.05055250296034366
###########################
mlj16 with cross validation
###########################
[0.95, 1.0, 0.9, 0.95, 1.0]
0.96
0.04183300132670378
#####################################
ek aur mlj11911 with cross validation
#####################################
[1.0, 1.0, 0.8666666666666667, 0.9333333333333333, 0.8333333333333334]
0.9266666666666665
0.07601169500660918
##########################
ek aur mljtune with tuning
##########################
---------------
RandomForestRegressor(max_depth = -1, …)
---------------
(best_model = RandomForestRegressor(max_depth = -1, …), best_history_entry = (model = RandomForestRegressor(max_depth = -1, …), measure = StatisticalMeasuresBase.RobustMeasure{StatisticalMeasuresBase.FussyMeasure{StatisticalMeasuresBase.RobustMeasure{StatisticalMeasuresBase.Multimeasure{StatisticalMeasuresBase.SupportsMissingsMeasure{StatisticalMeasures.LPLossOnScalars{Int64}}, Nothing, StatisticalMeasuresBase.Mean, typeof(identity)}}, Nothing}}[LPLoss(p = 2)], measurement = [20.885751992094864], per_fold = [[10.214466078431375, 14.541639207920786, 18.782057821782182, 44.53549976237625, 16.460753386138613]], evaluation = CompactPerformanceEvaluation(20.9,)), history = @NamedTuple{model::MLJDecisionTreeInterface.RandomForestRegressor, measure::Vector{StatisticalMeasuresBase.RobustMeasure{StatisticalMeasuresBase.FussyMeasure{StatisticalMeasuresBase.RobustMeasure{StatisticalMeasuresBase.Multimeasure{StatisticalMeasuresBase.SupportsMissingsMeasure{StatisticalMeasures.LPLossOnScalars{Int64}}, Nothing, StatisticalMeasuresBase.Mean, typeof(identity)}}, Nothing}}}, measurement::Vector{Float64}, per_fold::Vector{Vector{Float64}}, evaluation::CompactPerformanceEvaluation{MLJDecisionTreeInterface.RandomForestRegressor, Vector{StatisticalMeasuresBase.RobustMeasure{StatisticalMeasuresBase.FussyMeasure{StatisticalMeasuresBase.RobustMeasure{StatisticalMeasuresBase.Multimeasure{StatisticalMeasuresBase.SupportsMissingsMeasure{StatisticalMeasures.LPLossOnScalars{Int64}}, Nothing, StatisticalMeasuresBase.Mean, typeof(identity)}}, Nothing}}}, Vector{Float64}, Vector{typeof(MLJModelInterface.predict)}, Vector{Vector{Float64}}, Vector{Vector{Vector{Float64}}}, CV}}[(model = RandomForestRegressor(max_depth = -1, …), measure = [LPLoss(p = 2)], measurement = [22.740666732542824], per_fold = [[8.637005010893251, 19.056616611661163, 20.367266666666673, 49.365651485148526, 16.416434103410346]], evaluation = CompactPerformanceEvaluation(22.7,)), (model = RandomForestRegressor(max_depth = -1, …), measure = [LPLoss(p = 2)], measurement = [23.8732314229249], per_fold = [[10.602325490196083, 21.598650495049505, 23.361929702970297, 40.05386633663366, 23.880780198019796]], evaluation = CompactPerformanceEvaluation(23.9,)), (model = RandomForestRegressor(max_depth = -1, …), measure = [LPLoss(p = 2)], measurement = [22.164599480310347], per_fold = [[8.526565202130229, 13.819898398728768, 24.570775883143863, 45.0850347512529, 18.955753208654198]], evaluation = CompactPerformanceEvaluation(22.2,)), (model = RandomForestRegressor(max_depth = -1, …), measure = [LPLoss(p = 2)], measurement = [23.139314286561266], per_fold = [[8.995913970588235, 15.698436356435645, 26.420481257425745, 44.8765325940594, 19.84524092079208]], evaluation = CompactPerformanceEvaluation(23.1,)), (model = RandomForestRegressor(max_depth = -1, …), measure = [LPLoss(p = 2)], measurement = [23.65893483510375], per_fold = [[8.950685768995095, 15.902456203589121, 22.494960535272256, 43.638339789603954, 27.45385810643563]], evaluation = CompactPerformanceEvaluation(23.7,)), (model = RandomForestRegressor(max_depth = -1, …), measure = [LPLoss(p = 2)], measurement = [21.7517534201823], per_fold = [[8.682261604641852, 16.61578975550616, 23.638178824004846, 40.52957310567792, 19.422364720145485]], evaluation = CompactPerformanceEvaluation(21.8,)), (model = RandomForestRegressor(max_depth = -1, …), measure = [LPLoss(p = 2)], measurement = [20.885751992094864], per_fold = [[10.214466078431375, 14.541639207920786, 18.782057821782182, 44.53549976237625, 16.460753386138613]], evaluation = CompactPerformanceEvaluation(20.9,)), (model = RandomForestRegressor(max_depth = -1, …), measure = [LPLoss(p = 2)], measurement = [22.131873863636365], per_fold = [[7.356534497549021, 13.922850742574257, 21.713654888613863, 44.854412623762386, 22.95820705445544]], evaluation = CompactPerformanceEvaluation(22.1,)), (model = RandomForestRegressor(max_depth = -1, …), measure = [LPLoss(p = 2)], measurement = [22.66670415019763], per_fold = [[9.7908625, 19.76895, 25.4494542079208, 41.16495940594059, 17.286778217821784]], evaluation = CompactPerformanceEvaluation(22.7,)), (model = RandomForestRegressor(max_depth = -1, …), measure = [LPLoss(p = 2)], measurement = [24.019687390206407], per_fold = [[9.38820021786492, 16.838341529152917, 25.627061441144097, 48.53364062156215, 19.85605935093509]], evaluation = CompactPerformanceEvaluation(24.0,))], best_report = (features = [:Crim, :Zn, :Indus, :NOx, :Rm, :Age, :Dis, :Rad, :Tax, :PTRatio, :Black, :LStat],), plotting = (parameter_names = ["n_trees"], parameter_scales = [:linear], parameter_values = Any[30; 10; 90; 100; 80; 70; 50; 40; 20; 60;;], measurements = [22.740666732542824, 23.8732314229249, 22.164599480310347, 23.139314286561266, 23.65893483510375, 21.7517534201823, 20.885751992094864, 22.131873863636365, 22.66670415019763, 24.019687390206407]))
---------------
(best_model = RandomForestRegressor(max_depth = -1, …), best_fitted_params = (forest = Ensemble of Decision Trees
Trees: 50
Avg Leaves: 247.06
Avg Depth: 17.76,))
---------------
[20.885751992094864]
---------------
(model = RandomForestRegressor(max_depth = -1, …), measure = StatisticalMeasuresBase.RobustMeasure{StatisticalMeasuresBase.FussyMeasure{StatisticalMeasuresBase.RobustMeasure{StatisticalMeasuresBase.Multimeasure{StatisticalMeasuresBase.SupportsMissingsMeasure{StatisticalMeasures.LPLossOnScalars{Int64}}, Nothing, StatisticalMeasuresBase.Mean, typeof(identity)}}, Nothing}}[LPLoss(p = 2)], measurement = [20.885751992094864], per_fold = [[10.214466078431375, 14.541639207920786, 18.782057821782182, 44.53549976237625, 16.460753386138613]], evaluation = CompactPerformanceEvaluation(20.9,))
---------------
@NamedTuple{model::MLJDecisionTreeInterface.RandomForestRegressor, measure::Vector{StatisticalMeasuresBase.RobustMeasure{StatisticalMeasuresBase.FussyMeasure{StatisticalMeasuresBase.RobustMeasure{StatisticalMeasuresBase.Multimeasure{StatisticalMeasuresBase.SupportsMissingsMeasure{StatisticalMeasures.LPLossOnScalars{Int64}}, Nothing, StatisticalMeasuresBase.Mean, typeof(identity)}}, Nothing}}}, measurement::Vector{Float64}, per_fold::Vector{Vector{Float64}}, evaluation::CompactPerformanceEvaluation{MLJDecisionTreeInterface.RandomForestRegressor, Vector{StatisticalMeasuresBase.RobustMeasure{StatisticalMeasuresBase.FussyMeasure{StatisticalMeasuresBase.RobustMeasure{StatisticalMeasuresBase.Multimeasure{StatisticalMeasuresBase.SupportsMissingsMeasure{StatisticalMeasures.LPLossOnScalars{Int64}}, Nothing, StatisticalMeasuresBase.Mean, typeof(identity)}}, Nothing}}}, Vector{Float64}, Vector{typeof(MLJModelInterface.predict)}, Vector{Vector{Float64}}, Vector{Vector{Vector{Float64}}}, CV}}[(model = RandomForestRegressor(max_depth = -1, …), measure = [LPLoss(p = 2)], measurement = [22.740666732542824], per_fold = [[8.637005010893251, 19.056616611661163, 20.367266666666673, 49.365651485148526, 16.416434103410346]], evaluation = CompactPerformanceEvaluation(22.7,)), (model = RandomForestRegressor(max_depth = -1, …), measure = [LPLoss(p = 2)], measurement = [23.8732314229249], per_fold = [[10.602325490196083, 21.598650495049505, 23.361929702970297, 40.05386633663366, 23.880780198019796]], evaluation = CompactPerformanceEvaluation(23.9,)), (model = RandomForestRegressor(max_depth = -1, …), measure = [LPLoss(p = 2)], measurement = [22.164599480310347], per_fold = [[8.526565202130229, 13.819898398728768, 24.570775883143863, 45.0850347512529, 18.955753208654198]], evaluation = CompactPerformanceEvaluation(22.2,)), (model = RandomForestRegressor(max_depth = -1, …), measure = [LPLoss(p = 2)], measurement = [23.139314286561266], per_fold = [[8.995913970588235, 15.698436356435645, 26.420481257425745, 44.8765325940594, 19.84524092079208]], evaluation = CompactPerformanceEvaluation(23.1,)), (model = RandomForestRegressor(max_depth = -1, …), measure = [LPLoss(p = 2)], measurement = [23.65893483510375], per_fold = [[8.950685768995095, 15.902456203589121, 22.494960535272256, 43.638339789603954, 27.45385810643563]], evaluation = CompactPerformanceEvaluation(23.7,)), (model = RandomForestRegressor(max_depth = -1, …), measure = [LPLoss(p = 2)], measurement = [21.7517534201823], per_fold = [[8.682261604641852, 16.61578975550616, 23.638178824004846, 40.52957310567792, 19.422364720145485]], evaluation = CompactPerformanceEvaluation(21.8,)), (model = RandomForestRegressor(max_depth = -1, …), measure = [LPLoss(p = 2)], measurement = [20.885751992094864], per_fold = [[10.214466078431375, 14.541639207920786, 18.782057821782182, 44.53549976237625, 16.460753386138613]], evaluation = CompactPerformanceEvaluation(20.9,)), (model = RandomForestRegressor(max_depth = -1, …), measure = [LPLoss(p = 2)], measurement = [22.131873863636365], per_fold = [[7.356534497549021, 13.922850742574257, 21.713654888613863, 44.854412623762386, 22.95820705445544]], evaluation = CompactPerformanceEvaluation(22.1,)), (model = RandomForestRegressor(max_depth = -1, …), measure = [LPLoss(p = 2)], measurement = [22.66670415019763], per_fold = [[9.7908625, 19.76895, 25.4494542079208, 41.16495940594059, 17.286778217821784]], evaluation = CompactPerformanceEvaluation(22.7,)), (model = RandomForestRegressor(max_depth = -1, …), measure = [LPLoss(p = 2)], measurement = [24.019687390206407], per_fold = [[9.38820021786492, 16.838341529152917, 25.627061441144097, 48.53364062156215, 19.85605935093509]], evaluation = CompactPerformanceEvaluation(24.0,))]
RandomForestRegressor(max_depth = -1, …)
###################
mlj15 curve fitting
###################
All packages loaded successfully.
1:15 --- [37.06068977676915, 46.34033787266742, 69.54393490463906, 44.54533495128814, 73.28311957710237, 80.5294137715672, 79.24090025087456, 141.66766436385825, 144.02799909372706, 153.36189396281418, 187.34979593984932, 228.81142875260755, 217.04530680138464, 268.26007526906113, 295.1324663209221]
1.67556 + 50.7076*x - 18.5013*x^2 + 3.14092*x^3 - 0.216981*x^4 + 0.00537528*x^5
110.83570187826422
[42.61172335542394, 44.76709048817514, 50.223752217648915, 58.76091874225561, 70.15780026040558, 84.19360697050917, 100.64754907097672, 119.29883676021862, 139.92668023664515, 162.3102896986667, 186.22887534469362, 211.4616473731363, 237.78781598240496, 264.9865913709101, 292.83718373706193]
end maindata mining with decision tree
Test set accuracy: 0.94
Feature 3 < 2.6 ?
├─ 1 : 33/33
└─ Feature 4 < 1.75 ?
├─ Feature 3 < 5.35 ?
├─ 2 : 31/31
└─ 3 : 2/2
└─ Feature 3 < 4.85 ?
├─ Feature 2 < 3.1 ?
├─ 3 : 2/2
└─ 2 : 1/1
└─ 3 : 31/31plotting with makie
All packages loaded successfully.
6×3 DataFrame
Row │ x y1 y2
│ Float64 Float64 Float64
─────┼───────────────────────────────
1 │ -5.0 0.958924 0.283662
2 │ -4.9 0.982453 0.186512
3 │ -4.8 0.996165 0.087499
4 │ -4.7 0.999923 -0.0123887
5 │ -4.6 0.993691 -0.112153
6 │ -4.5 0.97753 -0.210796symbolics math symbolics
#############################
using Symbolics julia package
#############################
using symbolics
All packages loaded successfully.
-35 + 3x + 5y = 0
x - y = 0
[4.375, 4.375]
-123 - 7x - 5(x^2) + x^3
Differential(x)(-123 - 7x - 5(x^2) + x^3)
-7 - 10x + 3(x^2)
8x + 4(x^2)
x^6 + 6(x^5)*y + 15(x^4)*(y^2) + 20(x^3)*(y^3) + 15(x^2)*(y^4) + 6x*(y^5) + y^6symbolics math sympy
#########################
using SymPy julia package
#########################
All packages loaded successfully.
############################
solving non linear equations
############################
1
CRootOf(x^6 + x^5 + x^4 + x^3 + x^2 + x + 3, 0)
CRootOf(x^6 + x^5 + x^4 + x^3 + x^2 + x + 3, 1)
CRootOf(x^6 + x^5 + x^4 + x^3 + x^2 + x + 3, 2)
CRootOf(x^6 + x^5 + x^4 + x^3 + x^2 + x + 3, 3)
CRootOf(x^6 + x^5 + x^4 + x^3 + x^2 + x + 3, 4)
CRootOf(x^6 + x^5 + x^4 + x^3 + x^2 + x + 3, 5)
1
-1.1280707714543692 - 0.5876060832247132im
-1.1280707714543692 + 0.5876060832247132im
-0.16403887800672787 - 1.21306844998484im
-0.16403887800672787 + 1.21306844998484im
0.7921096494610971 - 0.7810724308829708im
0.7921096494610971 + 0.7810724308829708im
################################
solving derivative of a function
################################
3*x^2 - 7
#################################
solving integration of a function
#################################
x^4/4 - 7*x^2/2
###########################
solving non linear equation
###########################
-3/((-1/2 - sqrt(3)*I/2)*(27*sqrt(35) + 162)^(1/3)) - (-1/2 - sqrt(3)*I/2)*(27*sqrt(35) + 162)^(1/3)/3
-(-1/2 + sqrt(3)*I/2)*(27*sqrt(35) + 162)^(1/3)/3 - 3/((-1/2 + sqrt(3)*I/2)*(27*sqrt(35) + 162)^(1/3))
-(27*sqrt(35) + 162)^(1/3)/3 - 3/(27*sqrt(35) + 162)^(1/3)
1.3609461421185716 + 1.5989131324879002im
1.3609461421185716 - 1.5989131324879002im
-2.721892284237143
##################################
substituting values in a function
##################################
2*y^2 + y + 4
####################
expanding expression
####################
x^5 + 5*x^4*y + 10*x^3*y^2 + 10*x^2*y^3 + 5*x*y^4 + y^5
x^4 + 6*x^3 + 11*x^2 + 6*x
#######################
solving three variables
#######################
2
2trial & error calculations
#####################
iterative calculation
#####################
f1 = 0
1 0.0 -1.0e7 1.0e7 -145.0
2 5.0e6 0.0 1.0e7 1.2499992499997e20
3 2.5e6 0.0 5.0e6 1.5624981249985e19
4 1.25e6 0.0 2.5e6 1.9531203124924997e18
5 625000.0 0.0 1.25e6 2.4413945312124982e17
6 312500.0 0.0 625000.0 3.0517285154374856e16
7 156250.0 0.0 312500.0 3.814624022499855e15
8 78125.0 0.0 156250.0 4.76818847187355e14
9 39062.5 0.0 78125.0 5.9600066904151875e13
10 19531.25 0.0 39062.5 7.449436070411641e12
11 9765.625 0.0 19531.25 9.310364135818066e11
12 4882.8125 0.0 9765.625 1.1634376681132935e11
13 2441.40625 0.0 4882.8125 1.4534019041496735e10
14 1220.703125 0.0 2441.40625 1.8145115859689522e9
15 610.3515625 0.0 1220.703125 2.262522812443185e8
16 305.17578125 0.0 610.3515625 2.8140336603331864e7
17 152.587890625 0.0 305.17578125 3.48180395836059e6
18 76.2939453125 0.0 152.587890625 426024.1479041474
19 38.14697265625 0.0 76.2939453125 50771.69482681027
20 19.073486328125 0.0 38.14697265625 5588.0593438109645
21 9.5367431640625 0.0 19.073486328125 392.2928684721501
22 4.76837158203125 0.0 9.5367431640625 -133.40211487660667
23 7.152557373046875 4.76837158203125 9.5367431640625 24.525658051394885
24 5.9604644775390625 4.76837158203125 7.152557373046875 -75.58596041567432
25 6.556510925292969 5.9604644775390625 7.152557373046875 -31.452358893347537
26 6.854534149169922 6.556510925292969 7.152557373046875 -5.023311687583373
27 7.003545761108398 6.854534149169922 7.152557373046875 9.351256697903352
28 6.92903995513916 6.854534149169922 7.003545761108398 2.065234155078201
29 6.891787052154541 6.854534149169922 6.92903995513916 -1.5035682574081761
30 6.910413503646851 6.891787052154541 6.92903995513916 0.2746811890004892
31 6.901100277900696 6.891787052154541 6.910413503646851 -0.6159790507817888
32 6.905756890773773 6.901100277900696 6.910413503646851 -0.17103311295772983
33 6.908085197210312 6.905756890773773 6.910413503646851 0.05172795463928992
34 6.9069210439920425 6.905756890773773 6.908085197210312 -0.059676595271554334
35 6.907503120601177 6.9069210439920425 6.908085197210312 -0.003980324935838553
36 6.9077941589057446 6.907503120601177 6.908085197210312 0.02387231362286002
37 6.907648639753461 6.907503120601177 6.9077941589057446 0.009945619045481635
38 6.907575880177319 6.907503120601177 6.907648639753461 0.002982553231476004
39 6.907539500389248 6.907503120601177 6.907575880177319 -0.0004989093078791029
40 6.907557690283284 6.907539500389248 6.907575880177319 0.0012418160978882042
41 6.907548595336266 6.907539500389248 6.907557690283284 0.00037145192897014567
42 6.907544047862757 6.907539500389248 6.907548595336266 -6.372905590978917e-5
43 6.907546321599511 6.907544047862757 6.907548595336266 0.00015386134492700876
44 6.907545184731134 6.907544047862757 6.907546321599511 4.5066121600711995e-5
45 6.907544616296946 6.907544047862757 6.907545184731134 -9.331472881513037e-6
46 6.90754490051404 6.907544616296946 6.907545184731134 1.7867322924303153e-5
47 6.907544758405493 6.907544616296946 6.90754490051404 4.2679246519128355e-6
48 6.907544687351219 6.907544616296946 6.907544758405493 -2.531774185854374e-6
49 6.907544722878356 6.907544687351219 6.907544758405493 8.680752330292307e-7
50 6.907544705114788 6.907544687351219 6.907544722878356 -8.318495190451358e-7
51 6.907544713996572 6.907544705114788 6.907544722878356 1.811281435948331e-8
52 6.90754470955568 6.907544705114788 6.907544713996572 -4.068683097102621e-7
53 6.907544711776126 6.90754470955568 6.907544713996572 -1.9437777609709883e-7
54 6.907544712886349 6.907544711776126 6.907544713996572 -8.813245244709833e-8
55 6.90754471344146 6.907544712886349 6.907544713996572 -3.5009804832952796e-8
56 6.907544713719016 6.90754471344146 6.907544713996572 -8.448466815025313e-9
57 6.907544713857794 6.907544713719016 6.907544713996572 4.832173772228998e-9
58 6.907544713788405 6.907544713719016 6.907544713857794 -1.8081323105434421e-9
59 6.9075447138231 6.907544713788405 6.907544713857794 1.5120633634069236e-9
60 6.907544713805752 6.907544713788405 6.9075447138231 -1.4807710613240488e-10
61 6.907544713814426 6.907544713805752 6.9075447138231 6.819789177825442e-10
62 6.907544713810089 6.907544713805752 6.907544713814426 2.6696511667978484e-10
63 6.907544713807921 6.907544713805752 6.907544713810089 5.951505954726599e-11
converged to 6.907544713807921 after 63 iterationmakie plot
All packages loaded successfully.
### more with dataframe
["year,language", "1951,Regional Assembly Language", "1952,Autocode", "1954,IPL", "1955,FLOW-MATIC", "1957,FORTRAN", "1957,COMTRAN", "1958,LISP", "1958,ALGOL 58", "1959,FACT", "1959,COBOL", "1959,RPG", "1962,APL", "1962,Simula", "1962,SNOBOL", "1963,CPL", "1964,Speakeasy", "1964,BASIC", "1964,PL/I", "1966,JOSS", "1967,BCPL", "1968,Logo", "1969,B", "1970,Pascal", "1970,Forth", "1972,C", "1972,Smalltalk", "1972,Prolog", "1973,ML", "1975,Scheme", "1978,SQL ", "1980,C++ ", "1983,Ada", "1984,Common Lisp", "1984,MATLAB", "1984,dBase III", "1985,Eiffel", "1986,Objective-C", "1986,LabVIEW ", "1986,Erlang", "1987,Perl", "1988,Tcl", "1988,Wolfram Language ", "1989,FL ", "1990,Haskell", "1991,Python", "1991,Visual Basic", "1993,Lua", "1993,R", "1994,CLOS ", "1995,Ruby", "1995,Ada 95", "1995,Java", "1995,Delphi ", "1995,JavaScript", "1995,PHP", "1997,Rebol", "2000,ActionScript", "2001,C#", "2001,D", "2002,Scratch", "2003,Groovy", "2003,Scala", "2005,F#", "2006,PowerShell", "2007,Clojure", "2009,Go", "2010,Rust", "2011,Dart", "2011,Kotlin", "2011,Red", "2011,Elixir", "2012,Julia", "2014,Swift"]
73×2 DataFrame
Row │ year language
│ Int64 String31
─────┼───────────────────────────────────
1 │ 1951 Regional Assembly Language
2 │ 1952 Autocode
3 │ 1954 IPL
4 │ 1955 FLOW-MATIC
5 │ 1957 FORTRAN
6 │ 1957 COMTRAN
7 │ 1958 LISP
8 │ 1958 ALGOL 58
9 │ 1959 FACT
10 │ 1959 COBOL
11 │ 1959 RPG
12 │ 1962 APL
13 │ 1962 Simula
14 │ 1962 SNOBOL
15 │ 1963 CPL
16 │ 1964 Speakeasy
17 │ 1964 BASIC
18 │ 1964 PL/I
19 │ 1966 JOSS
20 │ 1967 BCPL
21 │ 1968 Logo
22 │ 1969 B
23 │ 1970 Pascal
24 │ 1970 Forth
25 │ 1972 C
26 │ 1972 Smalltalk
27 │ 1972 Prolog
28 │ 1973 ML
29 │ 1975 Scheme
30 │ 1978 SQL
31 │ 1980 C++
32 │ 1983 Ada
33 │ 1984 Common Lisp
34 │ 1984 MATLAB
35 │ 1984 dBase III
36 │ 1985 Eiffel
37 │ 1986 Objective-C
38 │ 1986 LabVIEW
39 │ 1986 Erlang
40 │ 1987 Perl
41 │ 1988 Tcl
42 │ 1988 Wolfram Language
43 │ 1989 FL
44 │ 1990 Haskell
45 │ 1991 Python
46 │ 1991 Visual Basic
47 │ 1993 Lua
48 │ 1993 R
49 │ 1994 CLOS
50 │ 1995 Ruby
51 │ 1995 Ada 95
52 │ 1995 Java
53 │ 1995 Delphi
54 │ 1995 JavaScript
55 │ 1995 PHP
56 │ 1997 Rebol
57 │ 2000 ActionScript
58 │ 2001 C#
59 │ 2001 D
60 │ 2002 Scratch
61 │ 2003 Groovy
62 │ 2003 Scala
63 │ 2005 F#
64 │ 2006 PowerShell
65 │ 2007 Clojure
66 │ 2009 Go
67 │ 2010 Rust
68 │ 2011 Dart
69 │ 2011 Kotlin
70 │ 2011 Red
71 │ 2011 Elixir
72 │ 2012 Julia
73 │ 2014 Swift
(73, 2)
2×7 DataFrame
Row │ variable mean min median max nmissing eltype
│ Symbol Union… Any Union… Any Int64 DataType
─────┼────────────────────────────────────────────────────────────────────
1 │ year 1982.99 1951 1986.0 2014 0 Int64
2 │ language ALGOL 58 dBase III 0 String31
4×2 DataFrame
Row │ year language
│ Int64 String31
─────┼─────────────────
1 │ 2011 Dart
2 │ 2011 Kotlin
3 │ 2011 Red
4 │ 2011 Elixir
2×2 DataFrame
Row │ year language
│ Int64 String31
─────┼─────────────────
1 │ 1993 R
2 │ 2011 Red
73×2 DataFrame
Row │ language cnt
│ String31 Int64
─────┼───────────────────────────────────
1 │ Regional Assembly Language 1
2 │ Autocode 1
3 │ IPL 1
4 │ FLOW-MATIC 1
5 │ FORTRAN 1
6 │ COMTRAN 1
7 │ LISP 1
8 │ ALGOL 58 1
9 │ FACT 1
10 │ COBOL 1
11 │ RPG 1
12 │ APL 1
13 │ Simula 1
14 │ SNOBOL 1
15 │ CPL 1
16 │ Speakeasy 1
17 │ BASIC 1
18 │ PL/I 1
19 │ JOSS 1
20 │ BCPL 1
21 │ Logo 1
22 │ B 1
23 │ Pascal 1
24 │ Forth 1
25 │ C 1
26 │ Smalltalk 1
27 │ Prolog 1
28 │ ML 1
29 │ Scheme 1
30 │ SQL 1
31 │ C++ 1
32 │ Ada 1
33 │ Common Lisp 1
34 │ MATLAB 1
35 │ dBase III 1
36 │ Eiffel 1
37 │ Objective-C 1
38 │ LabVIEW 1
39 │ Erlang 1
40 │ Perl 1
41 │ Tcl 1
42 │ Wolfram Language 1
43 │ FL 1
44 │ Haskell 1
45 │ Python 1
46 │ Visual Basic 1
47 │ Lua 1
48 │ R 1
49 │ CLOS 1
50 │ Ruby 1
51 │ Ada 95 1
52 │ Java 1
53 │ Delphi 1
54 │ JavaScript 1
55 │ PHP 1
56 │ Rebol 1
57 │ ActionScript 1
58 │ C# 1
59 │ D 1
60 │ Scratch 1
61 │ Groovy 1
62 │ Scala 1
63 │ F# 1
64 │ PowerShell 1
65 │ Clojure 1
66 │ Go 1
67 │ Rust 1
68 │ Dart 1
69 │ Kotlin 1
70 │ Red 1
71 │ Elixir 1
72 │ Julia 1
73 │ Swift 1
45×2 DataFrame
Row │ year cnt
│ Int64 Int64
─────┼──────────────
1 │ 1951 1
2 │ 1952 1
3 │ 1954 1
4 │ 1955 1
5 │ 1957 2
6 │ 1958 2
7 │ 1959 3
8 │ 1962 3
9 │ 1963 1
10 │ 1964 3
11 │ 1966 1
12 │ 1967 1
13 │ 1968 1
14 │ 1969 1
15 │ 1970 2
16 │ 1972 3
17 │ 1973 1
18 │ 1975 1
19 │ 1978 1
20 │ 1980 1
21 │ 1983 1
22 │ 1984 3
23 │ 1985 1
24 │ 1986 3
25 │ 1987 1
26 │ 1988 2
27 │ 1989 1
28 │ 1990 1
29 │ 1991 2
30 │ 1993 2
31 │ 1994 1
32 │ 1995 6
33 │ 1997 1
34 │ 2000 1
35 │ 2001 2
36 │ 2002 1
37 │ 2003 2
38 │ 2005 1
39 │ 2006 1
40 │ 2007 1
41 │ 2009 1
42 │ 2010 1
43 │ 2011 4
44 │ 2012 1
45 │ 2014 1
16×2 DataFrame
Row │ year cnt
│ Int64 Int64
─────┼──────────────
1 │ 1957 2
2 │ 1958 2
3 │ 1959 3
4 │ 1962 3
5 │ 1964 3
6 │ 1970 2
7 │ 1972 3
8 │ 1984 3
9 │ 1986 3
10 │ 1988 2
11 │ 1991 2
12 │ 1993 2
13 │ 1995 6
14 │ 2001 2
15 │ 2003 2
16 │ 2011 4
ggplot graphs from julia
transferring dataframe to R
forecasting
All packages loaded successfully.
scikit learn
All packages loaded successfully.
########################################
using logistic regression of scikitlearn
########################################
accuracy: 0.94
[0.9090909090909091, 0.9090909090909091, 0.9, 0.8888888888888888, 1.0]
0.9214141414141415 --- 0.044709950748680026
####################################
using Random forest from scikitlearn
####################################
[0.9090909090909091, 0.8181818181818182, 0.9, 0.8888888888888888, 1.0]
0.9032323232323233 --- 0.06490007073737401
#############################
using gradient boosting model
#############################
[0.9090909090909091, 0.8181818181818182, 0.9, 0.8888888888888888, 1.0]
0.9032323232323233 --- 0.06490007073737401
#########################
using decision tree model
#########################
[0.9090909090909091, 0.8181818181818182, 0.9, 0.8888888888888888, 1.0]
0.9032323232323233 --- 0.06490007073737401
##
hi
##fitting data using curvefit package
All packages loaded successfully.
fitted equation with polynomial :- 24.2809 + 5.00783*x - 2.77871*x^2 + 0.913072*x^3 - 0.0887008*x^4 + 0.00284204*x^5
given value:- 8.456
predicted value:- 89.37775043707526
16×5 DataFrame
Row │ x y yp diff diff2
│ Float64 Float64 Float64 Float64 Float64
─────┼─────────────────────────────────────────────────────
1 │ 0.0 23.2988 24.2809 0.982084 0.964489
2 │ 1.0 32.4526 27.3373 -5.1153 26.1663
3 │ 2.0 15.9293 29.1581 13.2287 174.999
4 │ 3.0 51.7123 32.4548 -19.2574 370.849
5 │ 4.0 28.6556 38.4924 9.83678 96.7623
6 │ 5.0 36.7128 47.4297 10.717 114.853
7 │ 6.0 72.6562 58.6614 -13.9948 195.856
8 │ 7.0 74.7773 71.1582 -3.61915 13.0983
9 │ 8.0 71.7125 83.8084 12.0959 146.312
10 │ 9.0 98.4208 95.7589 -2.66182 7.08529
11 │ 10.0 105.912 106.756 0.844395 0.713003
12 │ 11.0 131.397 117.487 -13.9102 193.494
13 │ 12.0 115.372 129.919 14.5467 211.607
14 │ 13.0 146.969 147.645 0.676121 0.457139
15 │ 14.0 182.962 176.219 -6.7432 45.4708
16 │ 15.0 221.127 223.501 2.3742 5.63681
1604.3242241468913
1:15 --- [37.06068977676915, 46.34033787266742, 69.54393490463906, 44.54533495128814, 73.28311957710237, 80.5294137715672, 79.24090025087456, 141.66766436385825, 144.02799909372706, 153.36189396281418, 187.34979593984932, 228.81142875260755, 217.04530680138464, 268.26007526906113, 295.1324663209221]
1.67556 + 50.7076*x - 18.5013*x^2 + 3.14092*x^3 - 0.216981*x^4 + 0.00537528*x^5
110.83570187826422
[42.61172335542394, 44.76709048817514, 50.223752217648915, 58.76091874225561, 70.15780026040558, 84.19360697050917, 100.64754907097672, 119.29883676021862, 139.92668023664515, 162.3102896986667, 186.22887534469362, 211.4616473731363, 237.78781598240496, 264.9865913709101, 292.83718373706193]"C:\\quarto27012025\\j135b.png"load images using quarto
### mlj revised
Accuracy: 0.92
ConfusionMatrix{3}([17 0 0; 0 16 2; 0 2 13])
Accuracy: 0.92
ConfusionMatrix{3}([17 0 0; 0 16 2; 0 2 13])
NeuralNetworkClassifier(layers = nothing, …)
***
*** Training for 200 epochs with algorithm ADAM.
Training.. avg loss on epoch 1 (1): 1.24504039176973
Training of 200 epoch completed. Final epoch error: 1.301778717558459.
Accuracy: 0.34
ConfusionMatrix{3}([17 18 15; 0 0 0; 0 0 0])
KNeighborsClassifier(n_neighbors = 5, …)
Accuracy: 0.92
ConfusionMatrix{3}([17 0 0; 0 15 1; 0 3 14])calling jupyter notebook & executing it
x
x^6 - 1 = 0
Sym{PyObject}[-1, 1, -1/2 - sqrt(3)*I/2, -1/2 + sqrt(3)*I/2, 1/2 - sqrt(3)*I/2, 1/2 + sqrt(3)*I/2]
-1
1
-1/2 - sqrt(3)*I/2
-1/2 + sqrt(3)*I/2
1/2 - sqrt(3)*I/2
1/2 + sqrt(3)*I/2
-1
1
-0.5 - 0.8660254037844386im
-0.5 + 0.8660254037844386im
0.5 - 0.8660254037844386im
0.5 + 0.8660254037844386im
(x - 1)*(x + 1)*(x^2 - x + 1)*(x^2 + x + 1)"# This file is machine-generated - editing it directly is not advised\njulia_version = \"1.10.5\"\nmanifest_format = \"2.0\"\nproject_hash = \"b5deeec79cbe7ef7ec3532a388bf78a5a547b2fb\"\n[[deps.ArgTools]]\nuuid = \"0dad84c5-d112-42e6-8d28-ef12dabb789f\"\nversion = \"1.1.1\"\n[[deps.Arti" ⋯ 39194 bytes ⋯ "l]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\", \"Wayland_jll\", \"Wayland_protocols_jll\", \"Xorg_libxcb_jll\", \"Xorg_xkeyboard_config_jll\"]\ngit-tree-sha1 = \"9c304562909ab2bab0262639bd4f444d7bc2be37\"\nuuid = \"d8fb68d0-12a3-5cfd-a85a-d49703b185fd\"\nversion = \"1.4.1+1\"\n"classification using cross validation
[1.0, 1.0, 0.8333333333333334, 0.9, 0.8]
0.9066666666666666
0.09249624617007737truekmean clustering with julia
All packages loaded successfully.
Cluster centers:
[5.005999999999999 6.853846153846153 5.88360655737705; 3.428000000000001 3.0769230769230766 2.740983606557377; 1.4620000000000002 5.715384615384615 4.388524590163935; 0.2459999999999999 2.053846153846153 1.4344262295081966]
Cluster assignments:
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 3, 3, 2, 2, 2, 2, 3, 2, 3, 2, 3, 2, 2, 3, 3, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 3, 2, 2, 2, 3, 2, 2, 2, 3, 2, 2, 3]
[1, 2, 3]
Dict(2 => 39, 3 => 61, 1 => 50)optimization with optim
All packages loaded successfully.
Optimal x: 12.25
Minimum cost: 37393.88linear regression using julia
All packages loaded successfully.
15×2 DataFrame
Row │ f1 f2
│ Int64 Float64
─────┼────────────────
1 │ 1 127.729
2 │ 2 151.553
3 │ 3 147.699
4 │ 4 194.152
5 │ 5 179.766
6 │ 6 194.493
7 │ 7 235.106
8 │ 8 239.897
9 │ 9 237.502
10 │ 10 262.881
11 │ 11 267.042
12 │ 12 287.197
13 │ 13 263.842
14 │ 14 286.109
15 │ 15 310.772
correlation= 0.9708659386641879
StatsModels.TableRegressionModel{LinearModel{GLM.LmResp{Vector{Float64}}, GLM.DensePredChol{Float64, CholeskyPivoted{Float64, Matrix{Float64}, Vector{Int64}}}}, Matrix{Float64}}
f2 ~ 1 + f1
Coefficients:
────────────────────────────────────────────────────────────────────────
Coef. Std. Error t Pr(>|t|) Lower 95% Upper 95%
────────────────────────────────────────────────────────────────────────
(Intercept) 127.358 7.65215 16.64 <1e-09 110.826 143.889
f1 12.2948 0.841625 14.61 <1e-08 10.4765 14.113
────────────────────────────────────────────────────────────────────────
StatsModels.TableRegressionModel{LinearModel{GLM.LmResp{Vector{Float64}}, GLM.DensePredChol{Float64, CholeskyPivoted{Float64, Matrix{Float64}, Vector{Int64}}}}, Matrix{Float64}}
Union{Missing, Float64}[212.06880375171306, 283.13254973153187]
2×3 DataFrame
Row │ f1 f2 fl
│ Float64 Float64? String
─────┼───────────────────────────
1 │ 6.89 212.069 *
2 │ 12.67 283.133 *
2×3 DataFrame
Row │ f1 f2 fl
│ Float64 Float64? String
─────┼───────────────────────────
1 │ 6.89 212.069 *
2 │ 12.67 283.133 *
17×3 DataFrame
Row │ f1 f2 fl
│ Float64 Float64? String
─────┼───────────────────────────
1 │ 1.0 127.729
2 │ 2.0 151.553
3 │ 3.0 147.699
4 │ 4.0 194.152
5 │ 5.0 179.766
6 │ 6.0 194.493
7 │ 6.89 212.069 *
8 │ 7.0 235.106
9 │ 8.0 239.897
10 │ 9.0 237.502
11 │ 10.0 262.881
12 │ 11.0 267.042
13 │ 12.0 287.197
14 │ 12.67 283.133 *
15 │ 13.0 263.842
16 │ 14.0 286.109
17 │ 15.0 310.772abc analysis with julia
4-element Vector{String}:
"Plots"
"DataFrames"
"CSV"
"Query"All packages loaded successfully.5×3 DataFrame
Row │ code name unitprice
│ String1 String15 Int64
─────┼────────────────────────────────
1 │ A AAAAAAAAAA 976
2 │ B BBBBBBBBBB 898
3 │ C CCCCCCCCCC 786
4 │ D DDDDDDDDDD 488
5 │ E EEEEEEEEEE 40130×2 DataFrame
Row │ code qty
│ String1 Int64
─────┼────────────────
1 │ A 21
2 │ B 9
3 │ A 10
4 │ A 21
5 │ A 19
6 │ E 17
7 │ B 13
8 │ C 22
⋮ │ ⋮ ⋮
24 │ B 11
25 │ E 23
26 │ A 7
27 │ D 23
28 │ D 18
29 │ B 8
30 │ A 13
15 rows omitted5×3 DataFrame
Row │ code name unitprice
│ String1 String15 Int64
─────┼────────────────────────────────
1 │ A AAAAAAAAAA 976
2 │ B BBBBBBBBBB 898
3 │ C CCCCCCCCCC 786
4 │ D DDDDDDDDDD 488
5 │ E EEEEEEEEEE 40130×2 DataFrame
Row │ code qty
│ String1 Int64
─────┼────────────────
1 │ A 21
2 │ B 9
3 │ A 10
4 │ A 21
5 │ A 19
6 │ E 17
7 │ B 13
8 │ C 22
9 │ D 22
10 │ A 14
11 │ D 21
12 │ D 15
13 │ E 6
14 │ B 25
15 │ A 11
16 │ A 18
17 │ E 17
18 │ C 9
19 │ A 8
20 │ E 16
21 │ E 5
22 │ D 15
23 │ A 11
24 │ B 11
25 │ E 23
26 │ A 7
27 │ D 23
28 │ D 18
29 │ B 8
30 │ A 1330×4 DataFrame
Row │ code name unitprice qty
│ String1 String15 Int64 Int64?
─────┼────────────────────────────────────────
1 │ A AAAAAAAAAA 976 21
2 │ B BBBBBBBBBB 898 9
3 │ A AAAAAAAAAA 976 10
4 │ A AAAAAAAAAA 976 21
5 │ A AAAAAAAAAA 976 19
6 │ E EEEEEEEEEE 401 17
7 │ B BBBBBBBBBB 898 13
8 │ C CCCCCCCCCC 786 22
⋮ │ ⋮ ⋮ ⋮ ⋮
24 │ B BBBBBBBBBB 898 11
25 │ E EEEEEEEEEE 401 23
26 │ A AAAAAAAAAA 976 7
27 │ D DDDDDDDDDD 488 23
28 │ D DDDDDDDDDD 488 18
29 │ B BBBBBBBBBB 898 8
30 │ A AAAAAAAAAA 976 13
15 rows omitted30-element Vector{Int64}:
20496
8082
9760
20496
18544
6817
11674
17292
10736
13664
⋮
7320
10736
9878
9223
6832
11224
8784
7184
1268830×5 DataFrame
Row │ code name unitprice qty cost
│ String1 String15 Int64 Int64? Int64
─────┼───────────────────────────────────────────────
1 │ A AAAAAAAAAA 976 21 20496
2 │ B BBBBBBBBBB 898 9 8082
3 │ A AAAAAAAAAA 976 10 9760
4 │ A AAAAAAAAAA 976 21 20496
5 │ A AAAAAAAAAA 976 19 18544
6 │ E EEEEEEEEEE 401 17 6817
7 │ B BBBBBBBBBB 898 13 11674
8 │ C CCCCCCCCCC 786 22 17292
9 │ D DDDDDDDDDD 488 22 10736
10 │ A AAAAAAAAAA 976 14 13664
11 │ D DDDDDDDDDD 488 21 10248
12 │ D DDDDDDDDDD 488 15 7320
13 │ E EEEEEEEEEE 401 6 2406
14 │ B BBBBBBBBBB 898 25 22450
15 │ A AAAAAAAAAA 976 11 10736
16 │ A AAAAAAAAAA 976 18 17568
17 │ E EEEEEEEEEE 401 17 6817
18 │ C CCCCCCCCCC 786 9 7074
19 │ A AAAAAAAAAA 976 8 7808
20 │ E EEEEEEEEEE 401 16 6416
21 │ E EEEEEEEEEE 401 5 2005
22 │ D DDDDDDDDDD 488 15 7320
23 │ A AAAAAAAAAA 976 11 10736
24 │ B BBBBBBBBBB 898 11 9878
25 │ E EEEEEEEEEE 401 23 9223
26 │ A AAAAAAAAAA 976 7 6832
27 │ D DDDDDDDDDD 488 23 11224
28 │ D DDDDDDDDDD 488 18 8784
29 │ B BBBBBBBBBB 898 8 7184
30 │ A AAAAAAAAAA 976 13 126885×2 DataFrame
Row │ name tcost
│ String15 Int64
─────┼────────────────────
1 │ AAAAAAAAAA 149328
2 │ BBBBBBBBBB 59268
3 │ EEEEEEEEEE 33684
4 │ CCCCCCCCCC 24366
5 │ DDDDDDDDDD 556325×2 DataFrame
Row │ name tcost
│ String15 Int64
─────┼────────────────────
1 │ AAAAAAAAAA 149328
2 │ BBBBBBBBBB 59268
3 │ EEEEEEEEEE 33684
4 │ CCCCCCCCCC 24366
5 │ DDDDDDDDDD 55632
goal programming
All packages loaded successfully.
opt_value = 295.0
opt_x1 = 125.0
opt_x2 = 12.5
opt_d1m = 0.0
opt_d1p = 37.5
opt_d2m = 0.0
opt_d2p = 0.0
opt_d3m = 0.0
opt_d3p = 257.5
opt_d4m = 0.0
opt_d4p = 0.0
fin profit= 400.0abc analysis
All packages loaded successfully.
30×4 DataFrame
Row │ code name unitprice qty
│ String1 String15 Int64 Int64?
─────┼────────────────────────────────────────
1 │ A AAAAAAAAAA 976 21
2 │ B BBBBBBBBBB 898 9
3 │ A AAAAAAAAAA 976 10
4 │ A AAAAAAAAAA 976 21
5 │ A AAAAAAAAAA 976 19
6 │ E EEEEEEEEEE 401 17
7 │ B BBBBBBBBBB 898 13
8 │ C CCCCCCCCCC 786 22
9 │ D DDDDDDDDDD 488 22
10 │ A AAAAAAAAAA 976 14
11 │ D DDDDDDDDDD 488 21
12 │ D DDDDDDDDDD 488 15
13 │ E EEEEEEEEEE 401 6
14 │ B BBBBBBBBBB 898 25
15 │ A AAAAAAAAAA 976 11
16 │ A AAAAAAAAAA 976 18
17 │ E EEEEEEEEEE 401 17
18 │ C CCCCCCCCCC 786 9
19 │ A AAAAAAAAAA 976 8
20 │ E EEEEEEEEEE 401 16
21 │ E EEEEEEEEEE 401 5
22 │ D DDDDDDDDDD 488 15
23 │ A AAAAAAAAAA 976 11
24 │ B BBBBBBBBBB 898 11
25 │ E EEEEEEEEEE 401 23
26 │ A AAAAAAAAAA 976 7
27 │ D DDDDDDDDDD 488 23
28 │ D DDDDDDDDDD 488 18
29 │ B BBBBBBBBBB 898 8
30 │ A AAAAAAAAAA 976 13
30×5 DataFrame
Row │ code name unitprice qty cost
│ String1 String15 Int64 Int64? Int64
─────┼───────────────────────────────────────────────
1 │ A AAAAAAAAAA 976 21 20496
2 │ B BBBBBBBBBB 898 9 8082
3 │ A AAAAAAAAAA 976 10 9760
4 │ A AAAAAAAAAA 976 21 20496
5 │ A AAAAAAAAAA 976 19 18544
6 │ E EEEEEEEEEE 401 17 6817
7 │ B BBBBBBBBBB 898 13 11674
8 │ C CCCCCCCCCC 786 22 17292
9 │ D DDDDDDDDDD 488 22 10736
10 │ A AAAAAAAAAA 976 14 13664
11 │ D DDDDDDDDDD 488 21 10248
12 │ D DDDDDDDDDD 488 15 7320
13 │ E EEEEEEEEEE 401 6 2406
14 │ B BBBBBBBBBB 898 25 22450
15 │ A AAAAAAAAAA 976 11 10736
16 │ A AAAAAAAAAA 976 18 17568
17 │ E EEEEEEEEEE 401 17 6817
18 │ C CCCCCCCCCC 786 9 7074
19 │ A AAAAAAAAAA 976 8 7808
20 │ E EEEEEEEEEE 401 16 6416
21 │ E EEEEEEEEEE 401 5 2005
22 │ D DDDDDDDDDD 488 15 7320
23 │ A AAAAAAAAAA 976 11 10736
24 │ B BBBBBBBBBB 898 11 9878
25 │ E EEEEEEEEEE 401 23 9223
26 │ A AAAAAAAAAA 976 7 6832
27 │ D DDDDDDDDDD 488 23 11224
28 │ D DDDDDDDDDD 488 18 8784
29 │ B BBBBBBBBBB 898 8 7184
30 │ A AAAAAAAAAA 976 13 12688
5×2 DataFrame
Row │ name tcost
│ String15 Int64
─────┼────────────────────
1 │ AAAAAAAAAA 149328
2 │ BBBBBBBBBB 59268
3 │ EEEEEEEEEE 33684
4 │ CCCCCCCCCC 24366
5 │ DDDDDDDDDD 55632
5×2 DataFrame
Row │ name tcost
│ String15 Int64
─────┼────────────────────
1 │ AAAAAAAAAA 149328
2 │ BBBBBBBBBB 59268
3 │ DDDDDDDDDD 55632
4 │ EEEEEEEEEE 33684
5 │ CCCCCCCCCC 24366
5×3 DataFrame
Row │ name tcost cum
│ String15 Int64 Int64
─────┼────────────────────────────
1 │ AAAAAAAAAA 149328 149328
2 │ BBBBBBBBBB 59268 208596
3 │ DDDDDDDDDD 55632 264228
4 │ EEEEEEEEEE 33684 297912
5 │ CCCCCCCCCC 24366 322278
5×4 DataFrame
Row │ name tcost cum cumper
│ String15 Int64 Int64 Float64
─────┼──────────────────────────────────────
1 │ AAAAAAAAAA 149328 149328 46.3352
2 │ BBBBBBBBBB 59268 208596 64.7255
3 │ DDDDDDDDDD 55632 264228 81.9876
4 │ EEEEEEEEEE 33684 297912 92.4394
5 │ CCCCCCCCCC 24366 322278 100.0using scikitlearn julia package
{julia j185 #| eval: false include("cif.jl")
using mlj julia package
All packages loaded successfully.
DecisionTreeClassifier(max_depth = -1, …)
#############################################
simple decision tree without cross validation
#############################################
Accuracy: 0.92
##########################################
simple decision tree with cross validation
##########################################
[0.8666666666666667, 0.9333333333333333, 0.9333333333333333, 0.9333333333333333, 0.9666666666666667]
Cross-Validation Results: PerformanceEvaluation(0.927,)
0.9266666666666667----0.036514837167011066testbase.jl
All packages loaded successfully.
231×3 DataFrame
Row │ name package_name is_supervised
│ String String Bool
─────┼────────────────────────────────────────────────────────────────────────────────
1 │ ABODDetector OutlierDetectionNeighbors false
2 │ ABODDetector OutlierDetectionPython false
3 │ ARDRegressor MLJScikitLearnInterface true
4 │ AdaBoostClassifier MLJScikitLearnInterface true
5 │ AdaBoostRegressor MLJScikitLearnInterface true
6 │ AdaBoostStumpClassifier DecisionTree true
7 │ AffinityPropagation MLJScikitLearnInterface false
8 │ AgglomerativeClustering MLJScikitLearnInterface false
9 │ AutoEncoder BetaML false
10 │ BM25Transformer MLJText false
11 │ BaggingClassifier MLJScikitLearnInterface true
12 │ BaggingRegressor MLJScikitLearnInterface true
13 │ BayesianLDA MLJScikitLearnInterface true
14 │ BayesianLDA MultivariateStats true
15 │ BayesianQDA MLJScikitLearnInterface true
16 │ BayesianRidgeRegressor MLJScikitLearnInterface true
17 │ BayesianSubspaceLDA MultivariateStats true
18 │ BernoulliNBClassifier MLJScikitLearnInterface true
19 │ Birch MLJScikitLearnInterface false
20 │ BisectingKMeans MLJScikitLearnInterface false
21 │ BorderlineSMOTE1 Imbalance false
22 │ CBLOFDetector OutlierDetectionPython false
23 │ CDDetector OutlierDetectionPython false
24 │ COFDetector OutlierDetectionNeighbors false
25 │ COFDetector OutlierDetectionPython false
26 │ COPODDetector OutlierDetectionPython false
27 │ CatBoostClassifier CatBoost true
28 │ CatBoostRegressor CatBoost true
29 │ ClusterUndersampler Imbalance false
30 │ ComplementNBClassifier MLJScikitLearnInterface true
31 │ ConstantClassifier MLJModels true
32 │ ConstantRegressor MLJModels true
33 │ ContinuousEncoder MLJModels false
34 │ CountTransformer MLJText false
35 │ DBSCAN Clustering false
36 │ DBSCAN MLJScikitLearnInterface false
37 │ DNNDetector OutlierDetectionNeighbors false
38 │ DecisionTreeClassifier BetaML true
39 │ DecisionTreeClassifier DecisionTree true
40 │ DecisionTreeRegressor BetaML true
41 │ DecisionTreeRegressor DecisionTree true
42 │ DeterministicConstantClassifier MLJModels true
43 │ DeterministicConstantRegressor MLJModels true
44 │ DummyClassifier MLJScikitLearnInterface true
45 │ DummyRegressor MLJScikitLearnInterface true
46 │ ECODDetector OutlierDetectionPython false
47 │ ENNUndersampler Imbalance false
48 │ ElasticNetCVRegressor MLJScikitLearnInterface true
49 │ ElasticNetRegressor MLJLinearModels true
50 │ ElasticNetRegressor MLJScikitLearnInterface true
51 │ EpsilonSVR LIBSVM true
52 │ EvoLinearRegressor EvoLinear true
53 │ EvoSplineRegressor EvoLinear true
54 │ EvoTreeClassifier EvoTrees true
55 │ EvoTreeCount EvoTrees true
56 │ EvoTreeGaussian EvoTrees true
57 │ EvoTreeMLE EvoTrees true
58 │ EvoTreeRegressor EvoTrees true
59 │ ExtraTreesClassifier MLJScikitLearnInterface true
60 │ ExtraTreesRegressor MLJScikitLearnInterface true
61 │ FactorAnalysis MultivariateStats false
62 │ FeatureAgglomeration MLJScikitLearnInterface false
63 │ FeatureSelector MLJModels false
64 │ FillImputer MLJModels false
65 │ GMMDetector OutlierDetectionPython false
66 │ GaussianMixtureClusterer BetaML false
67 │ GaussianMixtureImputer BetaML false
68 │ GaussianMixtureRegressor BetaML true
69 │ GaussianNBClassifier MLJScikitLearnInterface true
70 │ GaussianNBClassifier NaiveBayes true
71 │ GaussianProcessClassifier MLJScikitLearnInterface true
72 │ GaussianProcessRegressor MLJScikitLearnInterface true
73 │ GeneralImputer BetaML false
74 │ GradientBoostingClassifier MLJScikitLearnInterface true
75 │ GradientBoostingRegressor MLJScikitLearnInterface true
76 │ HBOSDetector OutlierDetectionPython false
77 │ HDBSCAN MLJScikitLearnInterface false
78 │ HierarchicalClustering Clustering false
79 │ HistGradientBoostingClassifier MLJScikitLearnInterface true
80 │ HistGradientBoostingRegressor MLJScikitLearnInterface true
81 │ HuberRegressor MLJLinearModels true
82 │ HuberRegressor MLJScikitLearnInterface true
83 │ ICA MultivariateStats false
84 │ IForestDetector OutlierDetectionPython false
85 │ INNEDetector OutlierDetectionPython false
86 │ ImageClassifier MLJFlux true
87 │ InteractionTransformer MLJModels false
88 │ KDEDetector OutlierDetectionPython false
89 │ KMeans Clustering false
90 │ KMeans MLJScikitLearnInterface false
91 │ KMeans ParallelKMeans false
92 │ KMeansClusterer BetaML false
93 │ KMedoids Clustering false
94 │ KMedoidsClusterer BetaML false
95 │ KNNClassifier NearestNeighborModels true
96 │ KNNDetector OutlierDetectionNeighbors false
97 │ KNNDetector OutlierDetectionPython false
98 │ KNNRegressor NearestNeighborModels true
99 │ KNeighborsClassifier MLJScikitLearnInterface true
100 │ KNeighborsRegressor MLJScikitLearnInterface true
101 │ KPLSRegressor PartialLeastSquaresRegressor true
102 │ KernelPCA MultivariateStats false
103 │ KernelPerceptronClassifier BetaML true
104 │ LADRegressor MLJLinearModels true
105 │ LDA MultivariateStats true
106 │ LGBMClassifier LightGBM true
107 │ LGBMRegressor LightGBM true
108 │ LMDDDetector OutlierDetectionPython false
109 │ LOCIDetector OutlierDetectionPython false
110 │ LODADetector OutlierDetectionPython false
111 │ LOFDetector OutlierDetectionNeighbors false
112 │ LOFDetector OutlierDetectionPython false
113 │ LarsCVRegressor MLJScikitLearnInterface true
114 │ LarsRegressor MLJScikitLearnInterface true
115 │ LassoCVRegressor MLJScikitLearnInterface true
116 │ LassoLarsCVRegressor MLJScikitLearnInterface true
117 │ LassoLarsICRegressor MLJScikitLearnInterface true
118 │ LassoLarsRegressor MLJScikitLearnInterface true
119 │ LassoRegressor MLJLinearModels true
120 │ LassoRegressor MLJScikitLearnInterface true
121 │ LinearBinaryClassifier GLM true
122 │ LinearCountRegressor GLM true
123 │ LinearRegressor GLM true
124 │ LinearRegressor MLJLinearModels true
125 │ LinearRegressor MLJScikitLearnInterface true
126 │ LinearRegressor MultivariateStats true
127 │ LinearSVC LIBSVM true
128 │ LogisticCVClassifier MLJScikitLearnInterface true
129 │ LogisticClassifier MLJLinearModels true
130 │ LogisticClassifier MLJScikitLearnInterface true
131 │ MCDDetector OutlierDetectionPython false
132 │ MeanShift MLJScikitLearnInterface false
133 │ MiniBatchKMeans MLJScikitLearnInterface false
134 │ MultiTaskElasticNetCVRegressor MLJScikitLearnInterface true
135 │ MultiTaskElasticNetRegressor MLJScikitLearnInterface true
136 │ MultiTaskLassoCVRegressor MLJScikitLearnInterface true
137 │ MultiTaskLassoRegressor MLJScikitLearnInterface true
138 │ MultinomialClassifier MLJLinearModels true
139 │ MultinomialNBClassifier MLJScikitLearnInterface true
140 │ MultinomialNBClassifier NaiveBayes true
141 │ MultitargetGaussianMixtureRegres… BetaML true
142 │ MultitargetKNNClassifier NearestNeighborModels true
143 │ MultitargetKNNRegressor NearestNeighborModels true
144 │ MultitargetLinearRegressor MultivariateStats true
145 │ MultitargetNeuralNetworkRegressor BetaML true
146 │ MultitargetNeuralNetworkRegressor MLJFlux true
147 │ MultitargetRidgeRegressor MultivariateStats true
148 │ MultitargetSRRegressor SymbolicRegression true
149 │ NeuralNetworkClassifier BetaML true
150 │ NeuralNetworkClassifier MLJFlux true
151 │ NeuralNetworkRegressor BetaML true
152 │ NeuralNetworkRegressor MLJFlux true
153 │ NuSVC LIBSVM true
154 │ NuSVR LIBSVM true
155 │ OCSVMDetector OutlierDetectionPython false
156 │ OPTICS MLJScikitLearnInterface false
157 │ OneClassSVM LIBSVM false
158 │ OneHotEncoder MLJModels false
159 │ OneRuleClassifier OneRule true
160 │ OrthogonalMatchingPursuitCVRegre… MLJScikitLearnInterface true
161 │ OrthogonalMatchingPursuitRegress… MLJScikitLearnInterface true
162 │ PCA MultivariateStats false
163 │ PCADetector OutlierDetectionPython false
164 │ PLSRegressor PartialLeastSquaresRegressor true
165 │ PPCA MultivariateStats false
166 │ PartLS PartitionedLS true
167 │ PassiveAggressiveClassifier MLJScikitLearnInterface true
168 │ PassiveAggressiveRegressor MLJScikitLearnInterface true
169 │ PegasosClassifier BetaML true
170 │ PerceptronClassifier BetaML true
171 │ PerceptronClassifier MLJScikitLearnInterface true
172 │ ProbabilisticNuSVC LIBSVM true
173 │ ProbabilisticSGDClassifier MLJScikitLearnInterface true
174 │ ProbabilisticSVC LIBSVM true
175 │ QuantileRegressor MLJLinearModels true
176 │ RANSACRegressor MLJScikitLearnInterface true
177 │ RODDetector OutlierDetectionPython false
178 │ ROSE Imbalance false
179 │ RandomForestClassifier BetaML true
180 │ RandomForestClassifier DecisionTree true
181 │ RandomForestClassifier MLJScikitLearnInterface true
182 │ RandomForestImputer BetaML false
183 │ RandomForestRegressor BetaML true
184 │ RandomForestRegressor DecisionTree true
185 │ RandomForestRegressor MLJScikitLearnInterface true
186 │ RandomOversampler Imbalance false
187 │ RandomUndersampler Imbalance false
188 │ RandomWalkOversampler Imbalance false
189 │ RidgeCVClassifier MLJScikitLearnInterface true
190 │ RidgeCVRegressor MLJScikitLearnInterface true
191 │ RidgeClassifier MLJScikitLearnInterface true
192 │ RidgeRegressor MLJLinearModels true
193 │ RidgeRegressor MLJScikitLearnInterface true
194 │ RidgeRegressor MultivariateStats true
195 │ RobustRegressor MLJLinearModels true
196 │ SGDClassifier MLJScikitLearnInterface true
197 │ SGDRegressor MLJScikitLearnInterface true
198 │ SMOTE Imbalance false
199 │ SMOTEN Imbalance false
200 │ SMOTENC Imbalance false
201 │ SODDetector OutlierDetectionPython false
202 │ SOSDetector OutlierDetectionPython false
203 │ SRRegressor SymbolicRegression true
204 │ SVC LIBSVM true
205 │ SVMClassifier MLJScikitLearnInterface true
206 │ SVMLinearClassifier MLJScikitLearnInterface true
207 │ SVMLinearRegressor MLJScikitLearnInterface true
208 │ SVMNuClassifier MLJScikitLearnInterface true
209 │ SVMNuRegressor MLJScikitLearnInterface true
210 │ SVMRegressor MLJScikitLearnInterface true
211 │ SelfOrganizingMap SelfOrganizingMaps false
212 │ SimpleImputer BetaML false
213 │ SpectralClustering MLJScikitLearnInterface false
214 │ StableForestClassifier SIRUS true
215 │ StableForestRegressor SIRUS true
216 │ StableRulesClassifier SIRUS true
217 │ StableRulesRegressor SIRUS true
218 │ Standardizer MLJModels false
219 │ SubspaceLDA MultivariateStats true
220 │ TSVDTransformer TSVD false
221 │ TfidfTransformer MLJText false
222 │ TheilSenRegressor MLJScikitLearnInterface true
223 │ TomekUndersampler Imbalance false
224 │ UnivariateBoxCoxTransformer MLJModels false
225 │ UnivariateDiscretizer MLJModels false
226 │ UnivariateFillImputer MLJModels false
227 │ UnivariateStandardizer MLJModels false
228 │ UnivariateTimeTypeToContinuous MLJModels false
229 │ XGBoostClassifier XGBoost true
230 │ XGBoostCount XGBoost true
231 │ XGBoostRegressor XGBoost true
231×3 DataFrame
Row │ name package_name is_supervised
│ String String Bool
─────┼────────────────────────────────────────────────────────────────────────────────
1 │ ABODDetector OutlierDetectionNeighbors false
2 │ ABODDetector OutlierDetectionPython false
3 │ ARDRegressor MLJScikitLearnInterface true
4 │ AdaBoostClassifier MLJScikitLearnInterface true
5 │ AdaBoostRegressor MLJScikitLearnInterface true
6 │ AdaBoostStumpClassifier DecisionTree true
7 │ AffinityPropagation MLJScikitLearnInterface false
8 │ AgglomerativeClustering MLJScikitLearnInterface false
9 │ AutoEncoder BetaML false
10 │ BM25Transformer MLJText false
11 │ BaggingClassifier MLJScikitLearnInterface true
12 │ BaggingRegressor MLJScikitLearnInterface true
13 │ BayesianLDA MLJScikitLearnInterface true
14 │ BayesianLDA MultivariateStats true
15 │ BayesianQDA MLJScikitLearnInterface true
16 │ BayesianRidgeRegressor MLJScikitLearnInterface true
17 │ BayesianSubspaceLDA MultivariateStats true
18 │ BernoulliNBClassifier MLJScikitLearnInterface true
19 │ Birch MLJScikitLearnInterface false
20 │ BisectingKMeans MLJScikitLearnInterface false
21 │ BorderlineSMOTE1 Imbalance false
22 │ CBLOFDetector OutlierDetectionPython false
23 │ CDDetector OutlierDetectionPython false
24 │ COFDetector OutlierDetectionNeighbors false
25 │ COFDetector OutlierDetectionPython false
26 │ COPODDetector OutlierDetectionPython false
27 │ CatBoostClassifier CatBoost true
28 │ CatBoostRegressor CatBoost true
29 │ ClusterUndersampler Imbalance false
30 │ ComplementNBClassifier MLJScikitLearnInterface true
31 │ ConstantClassifier MLJModels true
32 │ ConstantRegressor MLJModels true
33 │ ContinuousEncoder MLJModels false
34 │ CountTransformer MLJText false
35 │ DBSCAN Clustering false
36 │ DBSCAN MLJScikitLearnInterface false
37 │ DNNDetector OutlierDetectionNeighbors false
38 │ DecisionTreeClassifier BetaML true
39 │ DecisionTreeClassifier DecisionTree true
40 │ DecisionTreeRegressor BetaML true
41 │ DecisionTreeRegressor DecisionTree true
42 │ DeterministicConstantClassifier MLJModels true
43 │ DeterministicConstantRegressor MLJModels true
44 │ DummyClassifier MLJScikitLearnInterface true
45 │ DummyRegressor MLJScikitLearnInterface true
46 │ ECODDetector OutlierDetectionPython false
47 │ ENNUndersampler Imbalance false
48 │ ElasticNetCVRegressor MLJScikitLearnInterface true
49 │ ElasticNetRegressor MLJLinearModels true
50 │ ElasticNetRegressor MLJScikitLearnInterface true
51 │ EpsilonSVR LIBSVM true
52 │ EvoLinearRegressor EvoLinear true
53 │ EvoSplineRegressor EvoLinear true
54 │ EvoTreeClassifier EvoTrees true
55 │ EvoTreeCount EvoTrees true
56 │ EvoTreeGaussian EvoTrees true
57 │ EvoTreeMLE EvoTrees true
58 │ EvoTreeRegressor EvoTrees true
59 │ ExtraTreesClassifier MLJScikitLearnInterface true
60 │ ExtraTreesRegressor MLJScikitLearnInterface true
61 │ FactorAnalysis MultivariateStats false
62 │ FeatureAgglomeration MLJScikitLearnInterface false
63 │ FeatureSelector MLJModels false
64 │ FillImputer MLJModels false
65 │ GMMDetector OutlierDetectionPython false
66 │ GaussianMixtureClusterer BetaML false
67 │ GaussianMixtureImputer BetaML false
68 │ GaussianMixtureRegressor BetaML true
69 │ GaussianNBClassifier MLJScikitLearnInterface true
70 │ GaussianNBClassifier NaiveBayes true
71 │ GaussianProcessClassifier MLJScikitLearnInterface true
72 │ GaussianProcessRegressor MLJScikitLearnInterface true
73 │ GeneralImputer BetaML false
74 │ GradientBoostingClassifier MLJScikitLearnInterface true
75 │ GradientBoostingRegressor MLJScikitLearnInterface true
76 │ HBOSDetector OutlierDetectionPython false
77 │ HDBSCAN MLJScikitLearnInterface false
78 │ HierarchicalClustering Clustering false
79 │ HistGradientBoostingClassifier MLJScikitLearnInterface true
80 │ HistGradientBoostingRegressor MLJScikitLearnInterface true
81 │ HuberRegressor MLJLinearModels true
82 │ HuberRegressor MLJScikitLearnInterface true
83 │ ICA MultivariateStats false
84 │ IForestDetector OutlierDetectionPython false
85 │ INNEDetector OutlierDetectionPython false
86 │ ImageClassifier MLJFlux true
87 │ InteractionTransformer MLJModels false
88 │ KDEDetector OutlierDetectionPython false
89 │ KMeans Clustering false
90 │ KMeans MLJScikitLearnInterface false
91 │ KMeans ParallelKMeans false
92 │ KMeansClusterer BetaML false
93 │ KMedoids Clustering false
94 │ KMedoidsClusterer BetaML false
95 │ KNNClassifier NearestNeighborModels true
96 │ KNNDetector OutlierDetectionNeighbors false
97 │ KNNDetector OutlierDetectionPython false
98 │ KNNRegressor NearestNeighborModels true
99 │ KNeighborsClassifier MLJScikitLearnInterface true
100 │ KNeighborsRegressor MLJScikitLearnInterface true
101 │ KPLSRegressor PartialLeastSquaresRegressor true
102 │ KernelPCA MultivariateStats false
103 │ KernelPerceptronClassifier BetaML true
104 │ LADRegressor MLJLinearModels true
105 │ LDA MultivariateStats true
106 │ LGBMClassifier LightGBM true
107 │ LGBMRegressor LightGBM true
108 │ LMDDDetector OutlierDetectionPython false
109 │ LOCIDetector OutlierDetectionPython false
110 │ LODADetector OutlierDetectionPython false
111 │ LOFDetector OutlierDetectionNeighbors false
112 │ LOFDetector OutlierDetectionPython false
113 │ LarsCVRegressor MLJScikitLearnInterface true
114 │ LarsRegressor MLJScikitLearnInterface true
115 │ LassoCVRegressor MLJScikitLearnInterface true
116 │ LassoLarsCVRegressor MLJScikitLearnInterface true
117 │ LassoLarsICRegressor MLJScikitLearnInterface true
118 │ LassoLarsRegressor MLJScikitLearnInterface true
119 │ LassoRegressor MLJLinearModels true
120 │ LassoRegressor MLJScikitLearnInterface true
121 │ LinearBinaryClassifier GLM true
122 │ LinearCountRegressor GLM true
123 │ LinearRegressor GLM true
124 │ LinearRegressor MLJLinearModels true
125 │ LinearRegressor MLJScikitLearnInterface true
126 │ LinearRegressor MultivariateStats true
127 │ LinearSVC LIBSVM true
128 │ LogisticCVClassifier MLJScikitLearnInterface true
129 │ LogisticClassifier MLJLinearModels true
130 │ LogisticClassifier MLJScikitLearnInterface true
131 │ MCDDetector OutlierDetectionPython false
132 │ MeanShift MLJScikitLearnInterface false
133 │ MiniBatchKMeans MLJScikitLearnInterface false
134 │ MultiTaskElasticNetCVRegressor MLJScikitLearnInterface true
135 │ MultiTaskElasticNetRegressor MLJScikitLearnInterface true
136 │ MultiTaskLassoCVRegressor MLJScikitLearnInterface true
137 │ MultiTaskLassoRegressor MLJScikitLearnInterface true
138 │ MultinomialClassifier MLJLinearModels true
139 │ MultinomialNBClassifier MLJScikitLearnInterface true
140 │ MultinomialNBClassifier NaiveBayes true
141 │ MultitargetGaussianMixtureRegres… BetaML true
142 │ MultitargetKNNClassifier NearestNeighborModels true
143 │ MultitargetKNNRegressor NearestNeighborModels true
144 │ MultitargetLinearRegressor MultivariateStats true
145 │ MultitargetNeuralNetworkRegressor BetaML true
146 │ MultitargetNeuralNetworkRegressor MLJFlux true
147 │ MultitargetRidgeRegressor MultivariateStats true
148 │ MultitargetSRRegressor SymbolicRegression true
149 │ NeuralNetworkClassifier BetaML true
150 │ NeuralNetworkClassifier MLJFlux true
151 │ NeuralNetworkRegressor BetaML true
152 │ NeuralNetworkRegressor MLJFlux true
153 │ NuSVC LIBSVM true
154 │ NuSVR LIBSVM true
155 │ OCSVMDetector OutlierDetectionPython false
156 │ OPTICS MLJScikitLearnInterface false
157 │ OneClassSVM LIBSVM false
158 │ OneHotEncoder MLJModels false
159 │ OneRuleClassifier OneRule true
160 │ OrthogonalMatchingPursuitCVRegre… MLJScikitLearnInterface true
161 │ OrthogonalMatchingPursuitRegress… MLJScikitLearnInterface true
162 │ PCA MultivariateStats false
163 │ PCADetector OutlierDetectionPython false
164 │ PLSRegressor PartialLeastSquaresRegressor true
165 │ PPCA MultivariateStats false
166 │ PartLS PartitionedLS true
167 │ PassiveAggressiveClassifier MLJScikitLearnInterface true
168 │ PassiveAggressiveRegressor MLJScikitLearnInterface true
169 │ PegasosClassifier BetaML true
170 │ PerceptronClassifier BetaML true
171 │ PerceptronClassifier MLJScikitLearnInterface true
172 │ ProbabilisticNuSVC LIBSVM true
173 │ ProbabilisticSGDClassifier MLJScikitLearnInterface true
174 │ ProbabilisticSVC LIBSVM true
175 │ QuantileRegressor MLJLinearModels true
176 │ RANSACRegressor MLJScikitLearnInterface true
177 │ RODDetector OutlierDetectionPython false
178 │ ROSE Imbalance false
179 │ RandomForestClassifier BetaML true
180 │ RandomForestClassifier DecisionTree true
181 │ RandomForestClassifier MLJScikitLearnInterface true
182 │ RandomForestImputer BetaML false
183 │ RandomForestRegressor BetaML true
184 │ RandomForestRegressor DecisionTree true
185 │ RandomForestRegressor MLJScikitLearnInterface true
186 │ RandomOversampler Imbalance false
187 │ RandomUndersampler Imbalance false
188 │ RandomWalkOversampler Imbalance false
189 │ RidgeCVClassifier MLJScikitLearnInterface true
190 │ RidgeCVRegressor MLJScikitLearnInterface true
191 │ RidgeClassifier MLJScikitLearnInterface true
192 │ RidgeRegressor MLJLinearModels true
193 │ RidgeRegressor MLJScikitLearnInterface true
194 │ RidgeRegressor MultivariateStats true
195 │ RobustRegressor MLJLinearModels true
196 │ SGDClassifier MLJScikitLearnInterface true
197 │ SGDRegressor MLJScikitLearnInterface true
198 │ SMOTE Imbalance false
199 │ SMOTEN Imbalance false
200 │ SMOTENC Imbalance false
201 │ SODDetector OutlierDetectionPython false
202 │ SOSDetector OutlierDetectionPython false
203 │ SRRegressor SymbolicRegression true
204 │ SVC LIBSVM true
205 │ SVMClassifier MLJScikitLearnInterface true
206 │ SVMLinearClassifier MLJScikitLearnInterface true
207 │ SVMLinearRegressor MLJScikitLearnInterface true
208 │ SVMNuClassifier MLJScikitLearnInterface true
209 │ SVMNuRegressor MLJScikitLearnInterface true
210 │ SVMRegressor MLJScikitLearnInterface true
211 │ SelfOrganizingMap SelfOrganizingMaps false
212 │ SimpleImputer BetaML false
213 │ SpectralClustering MLJScikitLearnInterface false
214 │ StableForestClassifier SIRUS true
215 │ StableForestRegressor SIRUS true
216 │ StableRulesClassifier SIRUS true
217 │ StableRulesRegressor SIRUS true
218 │ Standardizer MLJModels false
219 │ SubspaceLDA MultivariateStats true
220 │ TSVDTransformer TSVD false
221 │ TfidfTransformer MLJText false
222 │ TheilSenRegressor MLJScikitLearnInterface true
223 │ TomekUndersampler Imbalance false
224 │ UnivariateBoxCoxTransformer MLJModels false
225 │ UnivariateDiscretizer MLJModels false
226 │ UnivariateFillImputer MLJModels false
227 │ UnivariateStandardizer MLJModels false
228 │ UnivariateTimeTypeToContinuous MLJModels false
229 │ XGBoostClassifier XGBoost true
230 │ XGBoostCount XGBoost true
231 │ XGBoostRegressor XGBoost true
#############################
using decisiontree classifier
#############################
0.94
┌──────────────┬────────┬────────────┬───────────┐
│ Actual_Class │ setosa │ versicolor │ virginica │
│ String │ Int64 │ Int64 │ Int64 │
├──────────────┼────────┼────────────┼───────────┤
│ setosa │ 19 │ 0 │ 0 │
│ versicolor │ 0 │ 17 │ 2 │
│ virginica │ 0 │ 1 │ 11 │
└──────────────┴────────┴────────────┴───────────┘
#####################################
using decisiontree classifier with cv
#####################################
machine(DecisionTreeClassifier(max_depth = -1, …), …)
Cross-validation results: PerformanceEvaluation(0.9,)
[1.0, 1.0, 0.8333333333333334, 0.9333333333333333, 0.7333333333333333]
Cross-Validation Results: PerformanceEvaluation(0.9,)
0.9----0.11547005383792516
#############################
using randomforest classifier
#############################
0.92
┌──────────────┬────────┬────────────┬───────────┐
│ Actual_Class │ setosa │ versicolor │ virginica │
│ String │ Int64 │ Int64 │ Int64 │
├──────────────┼────────┼────────────┼───────────┤
│ setosa │ 19 │ 0 │ 0 │
│ versicolor │ 0 │ 16 │ 3 │
│ virginica │ 0 │ 1 │ 11 │
└──────────────┴────────┴────────────┴───────────┘
#####################################
using randomforest classifier with cv
#####################################
machine(RandomForestClassifier(n_estimators = 100, …), …)
Cross-validation results: PerformanceEvaluation(0.907,)
[1.0, 1.0, 0.8666666666666667, 0.9333333333333333, 0.7333333333333333]
Cross-Validation Results: PerformanceEvaluation(0.907,)
0.9066666666666666----0.11155467020454343
#########################
using adaboost classifier
#########################
0.94
┌──────────────┬────────┬────────────┬───────────┐
│ Actual_Class │ setosa │ versicolor │ virginica │
│ String │ Int64 │ Int64 │ Int64 │
├──────────────┼────────┼────────────┼───────────┤
│ setosa │ 19 │ 0 │ 0 │
│ versicolor │ 0 │ 17 │ 2 │
│ virginica │ 0 │ 1 │ 11 │
└──────────────┴────────┴────────────┴───────────┘
#################################
using adaboost classifier with cv
#################################
machine(AdaBoostClassifier(estimator = nothing, …), …)
Cross-validation results: PerformanceEvaluation(0.913,)
[1.0, 1.0, 0.9, 0.9333333333333333, 0.7333333333333333]
Cross-Validation Results: PerformanceEvaluation(0.913,)
0.9133333333333333----0.10954451150103325
####################
using svm classifier
####################
0.94
┌──────────────┬────────┬────────────┬───────────┐
│ Actual_Class │ setosa │ versicolor │ virginica │
│ String │ Int64 │ Int64 │ Int64 │
├──────────────┼────────┼────────────┼───────────┤
│ setosa │ 19 │ 0 │ 0 │
│ versicolor │ 0 │ 16 │ 3 │
│ virginica │ 0 │ 0 │ 12 │
└──────────────┴────────┴────────────┴───────────┘
############################
using svm classifier with cv
############################
machine(SVMClassifier(C = 1.0, …), …)
Cross-validation results: PerformanceEvaluation(0.893,)
[1.0, 1.0, 0.8333333333333334, 0.9333333333333333, 0.7]
Cross-Validation Results: PerformanceEvaluation(0.893,)
0.8933333333333333----0.12780193008453877
####################################
using logistic regression classifier
####################################
0.96
┌──────────────┬────────┬────────────┬───────────┐
│ Actual_Class │ setosa │ versicolor │ virginica │
│ String │ Int64 │ Int64 │ Int64 │
├──────────────┼────────┼────────────┼───────────┤
│ setosa │ 19 │ 0 │ 0 │
│ versicolor │ 0 │ 17 │ 2 │
│ virginica │ 0 │ 0 │ 12 │
└──────────────┴────────┴────────────┴───────────┘
#############################################
using logistic regression classifier with cv
#############################################
machine(LogisticClassifier(penalty = l2, …), …)
Cross-validation results: PerformanceEvaluation(0.927,)
[1.0, 1.0, 0.8666666666666667, 0.9333333333333333, 0.8333333333333334]
Cross-Validation Results: PerformanceEvaluation(0.927,)
0.9266666666666665----0.07601169500660918
########################
using xgboost classifier
########################
0.94
┌──────────────┬────────┬────────────┬───────────┐
│ Actual_Class │ setosa │ versicolor │ virginica │
│ String │ Int64 │ Int64 │ Int64 │
├──────────────┼────────┼────────────┼───────────┤
│ setosa │ 19 │ 0 │ 0 │
│ versicolor │ 0 │ 17 │ 2 │
│ virginica │ 0 │ 1 │ 11 │
└──────────────┴────────┴────────────┴───────────┘
#####################################
using decisiontree classifier with cv
#####################################
machine(XGBoostClassifier(test = 1, …), …)
Cross-validation results: PerformanceEvaluation(0.907,)
[1.0, 1.0, 0.9, 0.9, 0.7333333333333333]
Cross-Validation Results: PerformanceEvaluation(0.907,)
0.9066666666666666----0.10903618155864087
#######################
using ridged classifier
#######################
0.78
┌──────────────┬────────┬────────────┬───────────┐
│ Actual_Class │ setosa │ versicolor │ virginica │
│ String │ Int64 │ Int64 │ Int64 │
├──────────────┼────────┼────────────┼───────────┤
│ setosa │ 19 │ 0 │ 0 │
│ versicolor │ 0 │ 12 │ 7 │
│ virginica │ 0 │ 4 │ 8 │
└──────────────┴────────┴────────────┴───────────┘
###############################
using ridge classifier with cv
###############################
machine(RidgeClassifier(alpha = 1.0, …), …)
Cross-validation results: PerformanceEvaluation(0.667,)
[1.0, 0.8, 0.23333333333333334, 0.7666666666666667, 0.5333333333333333]
Cross-Validation Results: PerformanceEvaluation(0.667,)
0.6666666666666666----0.29344694769431684
###################################
using knearest neighbour classifier
###################################
0.92
┌──────────────┬────────┬────────────┬───────────┐
│ Actual_Class │ setosa │ versicolor │ virginica │
│ String │ Int64 │ Int64 │ Int64 │
├──────────────┼────────┼────────────┼───────────┤
│ setosa │ 19 │ 0 │ 0 │
│ versicolor │ 0 │ 15 │ 4 │
│ virginica │ 0 │ 0 │ 12 │
└──────────────┴────────┴────────────┴───────────┘
###########################################
using knearest neighbour classifier with cv
###########################################
machine(KNeighborsClassifier(n_neighbors = 5, …), …)
Cross-validation results: PerformanceEvaluation(0.913,)
[1.0, 1.0, 0.8333333333333334, 0.9333333333333333, 0.8]
Cross-Validation Results: PerformanceEvaluation(0.913,)
0.9133333333333333----0.09309493362512625
########################
using bagging classifier
########################
0.94
┌──────────────┬────────┬────────────┬───────────┐
│ Actual_Class │ setosa │ versicolor │ virginica │
│ String │ Int64 │ Int64 │ Int64 │
├──────────────┼────────┼────────────┼───────────┤
│ setosa │ 19 │ 0 │ 0 │
│ versicolor │ 0 │ 17 │ 2 │
│ virginica │ 0 │ 1 │ 11 │
└──────────────┴────────┴────────────┴───────────┘
################################
using bagging classifier with cv
################################
machine(BaggingClassifier(estimator = nothing, …), …)
Cross-validation results: PerformanceEvaluation(0.92,)
[1.0, 1.0, 0.8666666666666667, 0.9333333333333333, 0.8]
Cross-Validation Results: PerformanceEvaluation(0.92,)
0.9199999999999999----0.0869226987360353hypertuning with MLJ1
1 1 1 1 1 1 0.667
1 1 2 1 1 1 0.667
1 1 3 1 1 1 0.667
1 1 4 1 1 1 0.667
1 1 5 1 1 1 0.667
1 2 1 1 1 1 0.667
1 2 2 1 1 1 0.667
1 2 3 1 1 1 0.667
1 2 4 1 1 1 0.667
1 2 5 1 1 1 0.667
1 3 1 1 1 1 0.667
1 3 2 1 1 1 0.667
1 3 3 1 1 1 0.667
1 3 4 1 1 1 0.667
1 3 5 1 1 1 0.667
1 4 1 1 1 1 0.667
1 4 2 1 1 1 0.667
1 4 3 1 1 1 0.667
1 4 4 1 1 1 0.667
1 4 5 1 1 1 0.667
1 5 1 1 1 1 0.667
1 5 2 1 1 1 0.667
1 5 3 1 1 1 0.667
1 5 4 1 1 1 0.667
1 5 5 1 1 1 0.667
2 1 1 2 1 1 0.96
2 1 2 2 1 1 0.96
2 1 3 2 1 1 0.96
2 1 4 2 1 1 0.96
2 1 5 2 1 1 0.96
2 2 1 2 1 1 0.96
2 2 2 2 1 1 0.96
2 2 3 2 1 1 0.96
2 2 4 2 1 1 0.96
2 2 5 2 1 1 0.96
2 3 1 2 1 1 0.96
2 3 2 2 1 1 0.96
2 3 3 2 1 1 0.96
2 3 4 2 1 1 0.96
2 3 5 2 1 1 0.96
2 4 1 2 1 1 0.96
2 4 2 2 1 1 0.96
2 4 3 2 1 1 0.96
2 4 4 2 1 1 0.96
2 4 5 2 1 1 0.96
2 5 1 2 1 1 0.96
2 5 2 2 1 1 0.96
2 5 3 2 1 1 0.96
2 5 4 2 1 1 0.96
2 5 5 2 1 1 0.96
3 1 1 3 1 1 0.973
3 1 2 3 1 2 0.973
3 1 3 3 1 3 0.973
3 1 4 3 1 4 0.973
3 1 5 3 1 5 0.973
3 2 1 3 2 1 0.973
3 2 2 3 2 2 0.973
3 2 3 3 2 3 0.973
3 2 4 3 2 4 0.973
3 2 5 3 2 5 0.973
3 3 1 3 3 1 0.973
3 3 2 3 3 2 0.973
3 3 3 3 3 3 0.973
3 3 4 3 3 4 0.973
3 3 5 3 3 5 0.973
3 4 1 3 4 1 0.973
3 4 2 3 4 2 0.973
3 4 3 3 4 3 0.973
3 4 4 3 4 4 0.973
3 4 5 3 4 5 0.973
3 5 1 3 5 1 0.973
3 5 2 3 5 2 0.973
3 5 3 3 5 3 0.973
3 5 4 3 5 4 0.973
3 5 5 3 5 5 0.973
4 1 1 4 1 1 0.993
4 1 2 4 1 1 0.993
4 1 3 4 1 1 0.993
4 1 4 4 1 1 0.993
4 1 5 4 1 1 0.993
4 2 1 4 2 1 0.993
4 2 2 4 2 1 0.993
4 2 3 4 2 1 0.993
4 2 4 4 2 1 0.993
4 2 5 4 2 1 0.993
4 3 1 4 3 1 0.993
4 3 2 4 3 1 0.993
4 3 3 4 3 1 0.993
4 3 4 4 3 1 0.993
4 3 5 4 3 1 0.993
4 4 1 4 3 1 0.993
4 4 2 4 3 1 0.993
4 4 3 4 3 1 0.993
4 4 4 4 3 1 0.993
4 4 5 4 3 1 0.993
4 5 1 4 3 1 0.993
4 5 2 4 3 1 0.993
4 5 3 4 3 1 0.993
4 5 4 4 3 1 0.993
4 5 5 4 3 1 0.993
5 1 1 5 1 1 1.0
5 1 2 5 1 1 1.0
5 1 3 5 1 1 1.0
5 1 4 5 1 1 1.0
5 1 5 5 1 1 1.0
5 2 1 5 1 1 1.0
5 2 2 5 1 1 1.0
5 2 3 5 1 1 1.0
5 2 4 5 1 1 1.0
5 2 5 5 1 1 1.0
5 3 1 5 1 1 1.0
5 3 2 5 1 1 1.0
5 3 3 5 1 1 1.0
5 3 4 5 1 1 1.0
5 3 5 5 1 1 1.0
5 4 1 5 1 1 1.0
5 4 2 5 1 1 1.0
5 4 3 5 1 1 1.0
5 4 4 5 1 1 1.0
5 4 5 5 1 1 1.0
5 5 1 5 1 1 1.0
5 5 2 5 1 1 1.0
5 5 3 5 1 1 1.0
5 5 4 5 1 1 1.0
5 5 5 5 1 1 1.0
6 1 1 5 1 1 1.0
6 1 2 5 1 1 1.0
6 1 3 5 1 1 1.0
6 1 4 5 1 1 1.0
6 1 5 5 1 1 1.0
6 2 1 5 1 1 1.0
6 2 2 5 1 1 1.0
6 2 3 5 1 1 1.0
6 2 4 5 1 1 1.0
6 2 5 5 1 1 1.0
6 3 1 5 1 1 1.0
6 3 2 5 1 1 1.0
6 3 3 5 1 1 1.0
6 3 4 5 1 1 1.0
6 3 5 5 1 1 1.0
6 4 1 5 1 1 1.0
6 4 2 5 1 1 1.0
6 4 3 5 1 1 1.0
6 4 4 5 1 1 1.0
6 4 5 5 1 1 1.0
6 5 1 5 1 1 1.0
6 5 2 5 1 1 1.0
6 5 3 5 1 1 1.0
6 5 4 5 1 1 1.0
6 5 5 5 1 1 1.0
7 1 1 5 1 1 1.0
7 1 2 5 1 1 1.0
7 1 3 5 1 1 1.0
7 1 4 5 1 1 1.0
7 1 5 5 1 1 1.0
7 2 1 5 1 1 1.0
7 2 2 5 1 1 1.0
7 2 3 5 1 1 1.0
7 2 4 5 1 1 1.0
7 2 5 5 1 1 1.0
7 3 1 5 1 1 1.0
7 3 2 5 1 1 1.0
7 3 3 5 1 1 1.0
7 3 4 5 1 1 1.0
7 3 5 5 1 1 1.0
7 4 1 5 1 1 1.0
7 4 2 5 1 1 1.0
7 4 3 5 1 1 1.0
7 4 4 5 1 1 1.0
7 4 5 5 1 1 1.0
7 5 1 5 1 1 1.0
7 5 2 5 1 1 1.0
7 5 3 5 1 1 1.0
7 5 4 5 1 1 1.0
7 5 5 5 1 1 1.0
8 1 1 5 1 1 1.0
8 1 2 5 1 1 1.0
8 1 3 5 1 1 1.0
8 1 4 5 1 1 1.0
8 1 5 5 1 1 1.0
8 2 1 5 1 1 1.0
8 2 2 5 1 1 1.0
8 2 3 5 1 1 1.0
8 2 4 5 1 1 1.0
8 2 5 5 1 1 1.0
8 3 1 5 1 1 1.0
8 3 2 5 1 1 1.0
8 3 3 5 1 1 1.0
8 3 4 5 1 1 1.0
8 3 5 5 1 1 1.0
8 4 1 5 1 1 1.0
8 4 2 5 1 1 1.0
8 4 3 5 1 1 1.0
8 4 4 5 1 1 1.0
8 4 5 5 1 1 1.0
8 5 1 5 1 1 1.0
8 5 2 5 1 1 1.0
8 5 3 5 1 1 1.0
8 5 4 5 1 1 1.0
8 5 5 5 1 1 1.0
9 1 1 5 1 1 1.0
9 1 2 5 1 1 1.0
9 1 3 5 1 1 1.0
9 1 4 5 1 1 1.0
9 1 5 5 1 1 1.0
9 2 1 5 1 1 1.0
9 2 2 5 1 1 1.0
9 2 3 5 1 1 1.0
9 2 4 5 1 1 1.0
9 2 5 5 1 1 1.0
9 3 1 5 1 1 1.0
9 3 2 5 1 1 1.0
9 3 3 5 1 1 1.0
9 3 4 5 1 1 1.0
9 3 5 5 1 1 1.0
9 4 1 5 1 1 1.0
9 4 2 5 1 1 1.0
9 4 3 5 1 1 1.0
9 4 4 5 1 1 1.0
9 4 5 5 1 1 1.0
9 5 1 5 1 1 1.0
9 5 2 5 1 1 1.0
9 5 3 5 1 1 1.0
9 5 4 5 1 1 1.0
9 5 5 5 1 1 1.0
10 1 1 5 1 1 1.0
10 1 2 5 1 1 1.0
10 1 3 5 1 1 1.0
10 1 4 5 1 1 1.0
10 1 5 5 1 1 1.0
10 2 1 5 1 1 1.0
10 2 2 5 1 1 1.0
10 2 3 5 1 1 1.0
10 2 4 5 1 1 1.0
10 2 5 5 1 1 1.0
10 3 1 5 1 1 1.0
10 3 2 5 1 1 1.0
10 3 3 5 1 1 1.0
10 3 4 5 1 1 1.0
10 3 5 5 1 1 1.0
10 4 1 5 1 1 1.0
10 4 2 5 1 1 1.0
10 4 3 5 1 1 1.0
10 4 4 5 1 1 1.0
10 4 5 5 1 1 1.0
10 5 1 5 1 1 1.0
10 5 2 5 1 1 1.0
10 5 3 5 1 1 1.0
10 5 4 5 1 1 1.0
10 5 5 5 1 1 1.0
5 1 1 1.0
best model accuracy:-1.0
┌──────────────┬────────┬────────────┬───────────┐
│ Actual_Class │ setosa │ versicolor │ virginica │
│ String │ Int64 │ Int64 │ Int64 │
├──────────────┼────────┼────────────┼───────────┤
│ setosa │ 50 │ 0 │ 0 │
│ versicolor │ 0 │ 50 │ 0 │
│ virginica │ 0 │ 0 │ 50 │
└──────────────┴────────┴────────────┴───────────┘
accuracy by using function in jm module
accuracy:-1.0xirr calculations
All packages loaded successfully.
2023-11-23 -11000
2023-12-08 -1000
2024-04-02 -38000
2025-03-31 53870
XIRR: 7.142322773631976
Adjusted Cash Flows: [-11000.0, -1000.0, -38000.0, 53846.98808073147]
Calculated IRR: 0.07100000000000024 include("query0403.jl")5×5 DataFrame
Row │ SepalLength SepalWidth PetalLength PetalWidth Species
│ Float64 Float64 Float64 Float64 Cat…
─────┼───────────────────────────────────────────────────────────
1 │ 5.1 3.5 1.4 0.2 setosa
2 │ 4.9 3.0 1.4 0.2 setosa
3 │ 4.7 3.2 1.3 0.2 setosa
4 │ 4.6 3.1 1.5 0.2 setosa
5 │ 5.0 3.6 1.4 0.2 setosa
61×2 DataFrame
Row │ Species SepalLength
│ Cat… Float64
─────┼─────────────────────────
1 │ versicolor 7.0
2 │ versicolor 6.4
3 │ versicolor 6.9
4 │ versicolor 6.5
5 │ versicolor 6.3
6 │ versicolor 6.6
7 │ versicolor 6.1
8 │ versicolor 6.7
9 │ versicolor 6.2
10 │ versicolor 6.1
11 │ versicolor 6.3
12 │ versicolor 6.1
13 │ versicolor 6.4
14 │ versicolor 6.6
15 │ versicolor 6.8
16 │ versicolor 6.7
17 │ versicolor 6.7
18 │ versicolor 6.3
19 │ versicolor 6.1
20 │ versicolor 6.2
21 │ virginica 6.3
22 │ virginica 7.1
23 │ virginica 6.3
24 │ virginica 6.5
25 │ virginica 7.6
26 │ virginica 7.3
27 │ virginica 6.7
28 │ virginica 7.2
29 │ virginica 6.5
30 │ virginica 6.4
31 │ virginica 6.8
32 │ virginica 6.4
33 │ virginica 6.5
34 │ virginica 7.7
35 │ virginica 7.7
36 │ virginica 6.9
37 │ virginica 7.7
38 │ virginica 6.3
39 │ virginica 6.7
40 │ virginica 7.2
41 │ virginica 6.2
42 │ virginica 6.1
43 │ virginica 6.4
44 │ virginica 7.2
45 │ virginica 7.4
46 │ virginica 7.9
47 │ virginica 6.4
48 │ virginica 6.3
49 │ virginica 6.1
50 │ virginica 7.7
51 │ virginica 6.3
52 │ virginica 6.4
53 │ virginica 6.9
54 │ virginica 6.7
55 │ virginica 6.9
56 │ virginica 6.8
57 │ virginica 6.7
58 │ virginica 6.7
59 │ virginica 6.3
60 │ virginica 6.5
61 │ virginica 6.2
22×5 DataFrame
Row │ SepalLength SepalWidth PetalLength PetalWidth Species
│ Float64 Float64 Float64 Float64 Cat…
─────┼─────────────────────────────────────────────────────────────
1 │ 7.1 3.0 5.9 2.1 virginica
2 │ 7.6 3.0 6.6 2.1 virginica
3 │ 7.3 2.9 6.3 1.8 virginica
4 │ 6.7 2.5 5.8 1.8 virginica
5 │ 7.2 3.6 6.1 2.5 virginica
6 │ 6.8 3.0 5.5 2.1 virginica
7 │ 7.7 3.8 6.7 2.2 virginica
8 │ 7.7 2.6 6.9 2.3 virginica
9 │ 6.9 3.2 5.7 2.3 virginica
10 │ 7.7 2.8 6.7 2.0 virginica
11 │ 6.7 3.3 5.7 2.1 virginica
12 │ 7.2 3.2 6.0 1.8 virginica
13 │ 7.2 3.0 5.8 1.6 virginica
14 │ 7.4 2.8 6.1 1.9 virginica
15 │ 7.9 3.8 6.4 2.0 virginica
16 │ 7.7 3.0 6.1 2.3 virginica
17 │ 6.9 3.1 5.4 2.1 virginica
18 │ 6.7 3.1 5.6 2.4 virginica
19 │ 6.9 3.1 5.1 2.3 virginica
20 │ 6.8 3.2 5.9 2.3 virginica
21 │ 6.7 3.3 5.7 2.5 virginica
22 │ 6.7 3.0 5.2 2.3 virginica
3×2 DataFrame
Row │ Species Count
│ String Int64
─────┼────────────────
1 │ Cat 4
2 │ Bird 2
3 │ Dog 2
3×2 DataFrame
Row │ Species Count
│ Cat… Int64
─────┼───────────────────
1 │ setosa 50
2 │ versicolor 50
3 │ virginica 50
2×2 DataFrame
Row │ name number_of_children
│ String Int64
─────┼────────────────────────────
1 │ Sally 2
2 │ Roger 4
1×3 DataFrame
Row │ name children age
│ String Int64 Float64
─────┼───────────────────────────
1 │ Kirk 2 59.0learnng sqlite
include("sqlite1503.jl")All packages loaded successfully.
true
SQLite.DBTable[SQLite.DBTable("iris", Tables.Schema:
:? Union{Missing, String}
:sepal_length Union{Missing, String}
:sepal_width Union{Missing, String}
:petal_length Union{Missing, String}
:petal_width Union{Missing, String}
:species Union{Missing, String}), SQLite.DBTable("iris_table", Tables.Schema:
:Column1 Union{Missing, Int64}
:sepal_length Union{Missing, Float64}
:sepal_width Union{Missing, Float64}
:petal_length Union{Missing, Float64}
:petal_width Union{Missing, Float64}
:species Union{Missing, String})]
150×6 DataFrame
Row │ ? sepal_length sepal_width petal_length petal_width species
│ String String String String String String
─────┼──────────────────────────────────────────────────────────────────────────
1 │ 1 5.1 3.5 1.4 0.2 setosa
2 │ 2 4.9 3 1.4 0.2 setosa
3 │ 3 4.7 3.2 1.3 0.2 setosa
4 │ 4 4.6 3.1 1.5 0.2 setosa
5 │ 5 5 3.6 1.4 0.2 setosa
6 │ 6 5.4 3.9 1.7 0.4 setosa
7 │ 7 4.6 3.4 1.4 0.3 setosa
8 │ 8 5 3.4 1.5 0.2 setosa
9 │ 9 4.4 2.9 1.4 0.2 setosa
10 │ 10 4.9 3.1 1.5 0.1 setosa
11 │ 11 5.4 3.7 1.5 0.2 setosa
12 │ 12 4.8 3.4 1.6 0.2 setosa
13 │ 13 4.8 3 1.4 0.1 setosa
14 │ 14 4.3 3 1.1 0.1 setosa
15 │ 15 5.8 4 1.2 0.2 setosa
16 │ 16 5.7 4.4 1.5 0.4 setosa
17 │ 17 5.4 3.9 1.3 0.4 setosa
18 │ 18 5.1 3.5 1.4 0.3 setosa
19 │ 19 5.7 3.8 1.7 0.3 setosa
20 │ 20 5.1 3.8 1.5 0.3 setosa
21 │ 21 5.4 3.4 1.7 0.2 setosa
22 │ 22 5.1 3.7 1.5 0.4 setosa
23 │ 23 4.6 3.6 1 0.2 setosa
24 │ 24 5.1 3.3 1.7 0.5 setosa
25 │ 25 4.8 3.4 1.9 0.2 setosa
26 │ 26 5 3 1.6 0.2 setosa
27 │ 27 5 3.4 1.6 0.4 setosa
28 │ 28 5.2 3.5 1.5 0.2 setosa
29 │ 29 5.2 3.4 1.4 0.2 setosa
30 │ 30 4.7 3.2 1.6 0.2 setosa
31 │ 31 4.8 3.1 1.6 0.2 setosa
32 │ 32 5.4 3.4 1.5 0.4 setosa
33 │ 33 5.2 4.1 1.5 0.1 setosa
34 │ 34 5.5 4.2 1.4 0.2 setosa
35 │ 35 4.9 3.1 1.5 0.2 setosa
36 │ 36 5 3.2 1.2 0.2 setosa
37 │ 37 5.5 3.5 1.3 0.2 setosa
38 │ 38 4.9 3.6 1.4 0.1 setosa
39 │ 39 4.4 3 1.3 0.2 setosa
40 │ 40 5.1 3.4 1.5 0.2 setosa
41 │ 41 5 3.5 1.3 0.3 setosa
42 │ 42 4.5 2.3 1.3 0.3 setosa
43 │ 43 4.4 3.2 1.3 0.2 setosa
44 │ 44 5 3.5 1.6 0.6 setosa
45 │ 45 5.1 3.8 1.9 0.4 setosa
46 │ 46 4.8 3 1.4 0.3 setosa
47 │ 47 5.1 3.8 1.6 0.2 setosa
48 │ 48 4.6 3.2 1.4 0.2 setosa
49 │ 49 5.3 3.7 1.5 0.2 setosa
50 │ 50 5 3.3 1.4 0.2 setosa
51 │ 51 7 3.2 4.7 1.4 versicolor
52 │ 52 6.4 3.2 4.5 1.5 versicolor
53 │ 53 6.9 3.1 4.9 1.5 versicolor
54 │ 54 5.5 2.3 4 1.3 versicolor
55 │ 55 6.5 2.8 4.6 1.5 versicolor
56 │ 56 5.7 2.8 4.5 1.3 versicolor
57 │ 57 6.3 3.3 4.7 1.6 versicolor
58 │ 58 4.9 2.4 3.3 1 versicolor
59 │ 59 6.6 2.9 4.6 1.3 versicolor
60 │ 60 5.2 2.7 3.9 1.4 versicolor
61 │ 61 5 2 3.5 1 versicolor
62 │ 62 5.9 3 4.2 1.5 versicolor
63 │ 63 6 2.2 4 1 versicolor
64 │ 64 6.1 2.9 4.7 1.4 versicolor
65 │ 65 5.6 2.9 3.6 1.3 versicolor
66 │ 66 6.7 3.1 4.4 1.4 versicolor
67 │ 67 5.6 3 4.5 1.5 versicolor
68 │ 68 5.8 2.7 4.1 1 versicolor
69 │ 69 6.2 2.2 4.5 1.5 versicolor
70 │ 70 5.6 2.5 3.9 1.1 versicolor
71 │ 71 5.9 3.2 4.8 1.8 versicolor
72 │ 72 6.1 2.8 4 1.3 versicolor
73 │ 73 6.3 2.5 4.9 1.5 versicolor
74 │ 74 6.1 2.8 4.7 1.2 versicolor
75 │ 75 6.4 2.9 4.3 1.3 versicolor
76 │ 76 6.6 3 4.4 1.4 versicolor
77 │ 77 6.8 2.8 4.8 1.4 versicolor
78 │ 78 6.7 3 5 1.7 versicolor
79 │ 79 6 2.9 4.5 1.5 versicolor
80 │ 80 5.7 2.6 3.5 1 versicolor
81 │ 81 5.5 2.4 3.8 1.1 versicolor
82 │ 82 5.5 2.4 3.7 1 versicolor
83 │ 83 5.8 2.7 3.9 1.2 versicolor
84 │ 84 6 2.7 5.1 1.6 versicolor
85 │ 85 5.4 3 4.5 1.5 versicolor
86 │ 86 6 3.4 4.5 1.6 versicolor
87 │ 87 6.7 3.1 4.7 1.5 versicolor
88 │ 88 6.3 2.3 4.4 1.3 versicolor
89 │ 89 5.6 3 4.1 1.3 versicolor
90 │ 90 5.5 2.5 4 1.3 versicolor
91 │ 91 5.5 2.6 4.4 1.2 versicolor
92 │ 92 6.1 3 4.6 1.4 versicolor
93 │ 93 5.8 2.6 4 1.2 versicolor
94 │ 94 5 2.3 3.3 1 versicolor
95 │ 95 5.6 2.7 4.2 1.3 versicolor
96 │ 96 5.7 3 4.2 1.2 versicolor
97 │ 97 5.7 2.9 4.2 1.3 versicolor
98 │ 98 6.2 2.9 4.3 1.3 versicolor
99 │ 99 5.1 2.5 3 1.1 versicolor
100 │ 100 5.7 2.8 4.1 1.3 versicolor
101 │ 101 6.3 3.3 6 2.5 virginica
102 │ 102 5.8 2.7 5.1 1.9 virginica
103 │ 103 7.1 3 5.9 2.1 virginica
104 │ 104 6.3 2.9 5.6 1.8 virginica
105 │ 105 6.5 3 5.8 2.2 virginica
106 │ 106 7.6 3 6.6 2.1 virginica
107 │ 107 4.9 2.5 4.5 1.7 virginica
108 │ 108 7.3 2.9 6.3 1.8 virginica
109 │ 109 6.7 2.5 5.8 1.8 virginica
110 │ 110 7.2 3.6 6.1 2.5 virginica
111 │ 111 6.5 3.2 5.1 2 virginica
112 │ 112 6.4 2.7 5.3 1.9 virginica
113 │ 113 6.8 3 5.5 2.1 virginica
114 │ 114 5.7 2.5 5 2 virginica
115 │ 115 5.8 2.8 5.1 2.4 virginica
116 │ 116 6.4 3.2 5.3 2.3 virginica
117 │ 117 6.5 3 5.5 1.8 virginica
118 │ 118 7.7 3.8 6.7 2.2 virginica
119 │ 119 7.7 2.6 6.9 2.3 virginica
120 │ 120 6 2.2 5 1.5 virginica
121 │ 121 6.9 3.2 5.7 2.3 virginica
122 │ 122 5.6 2.8 4.9 2 virginica
123 │ 123 7.7 2.8 6.7 2 virginica
124 │ 124 6.3 2.7 4.9 1.8 virginica
125 │ 125 6.7 3.3 5.7 2.1 virginica
126 │ 126 7.2 3.2 6 1.8 virginica
127 │ 127 6.2 2.8 4.8 1.8 virginica
128 │ 128 6.1 3 4.9 1.8 virginica
129 │ 129 6.4 2.8 5.6 2.1 virginica
130 │ 130 7.2 3 5.8 1.6 virginica
131 │ 131 7.4 2.8 6.1 1.9 virginica
132 │ 132 7.9 3.8 6.4 2 virginica
133 │ 133 6.4 2.8 5.6 2.2 virginica
134 │ 134 6.3 2.8 5.1 1.5 virginica
135 │ 135 6.1 2.6 5.6 1.4 virginica
136 │ 136 7.7 3 6.1 2.3 virginica
137 │ 137 6.3 3.4 5.6 2.4 virginica
138 │ 138 6.4 3.1 5.5 1.8 virginica
139 │ 139 6 3 4.8 1.8 virginica
140 │ 140 6.9 3.1 5.4 2.1 virginica
141 │ 141 6.7 3.1 5.6 2.4 virginica
142 │ 142 6.9 3.1 5.1 2.3 virginica
143 │ 143 5.8 2.7 5.1 1.9 virginica
144 │ 144 6.8 3.2 5.9 2.3 virginica
145 │ 145 6.7 3.3 5.7 2.5 virginica
146 │ 146 6.7 3 5.2 2.3 virginica
147 │ 147 6.3 2.5 5 1.9 virginica
148 │ 148 6.5 3 5.2 2 virginica
149 │ 149 6.2 3.4 5.4 2.3 virginica
150 │ 150 5.9 3 5.1 1.8 virginica
8×6 DataFrame
Row │ ? sepal_length sepal_width petal_length petal_width species
│ String String String String String String
─────┼──────────────────────────────────────────────────────────────────────────
1 │ 51 7 3.2 4.7 1.4 versicolor
2 │ 53 6.9 3.1 4.9 1.5 versicolor
3 │ 77 6.8 2.8 4.8 1.4 versicolor
4 │ 66 6.7 3.1 4.4 1.4 versicolor
5 │ 78 6.7 3 5 1.7 versicolor
6 │ 87 6.7 3.1 4.7 1.5 versicolor
7 │ 59 6.6 2.9 4.6 1.3 versicolor
8 │ 76 6.6 3 4.4 1.4 versicolor
3×4 DataFrame
Row │ species count slavg slsum
│ String Int64 Float64 Float64
─────┼─────────────────────────────────────
1 │ setosa 50 5.006 250.3
2 │ versicolor 50 5.936 296.8
3 │ virginica 50 6.588 329.4
true
2×2 DataFrame
Row │ species count
│ String Int64
─────┼───────────────────
1 │ versicolor 20
2 │ virginica 41
150×6 DataFrame
Row │ Column1 sepal_length sepal_width petal_length petal_width species
│ Int64 Float64 Float64 Float64 Float64 String15
─────┼───────────────────────────────────────────────────────────────────────────
1 │ 1 5.1 3.5 1.4 0.2 setosa
2 │ 2 4.9 3.0 1.4 0.2 setosa
3 │ 3 4.7 3.2 1.3 0.2 setosa
4 │ 4 4.6 3.1 1.5 0.2 setosa
5 │ 5 5.0 3.6 1.4 0.2 setosa
6 │ 6 5.4 3.9 1.7 0.4 setosa
7 │ 7 4.6 3.4 1.4 0.3 setosa
8 │ 8 5.0 3.4 1.5 0.2 setosa
9 │ 9 4.4 2.9 1.4 0.2 setosa
10 │ 10 4.9 3.1 1.5 0.1 setosa
11 │ 11 5.4 3.7 1.5 0.2 setosa
12 │ 12 4.8 3.4 1.6 0.2 setosa
13 │ 13 4.8 3.0 1.4 0.1 setosa
14 │ 14 4.3 3.0 1.1 0.1 setosa
15 │ 15 5.8 4.0 1.2 0.2 setosa
16 │ 16 5.7 4.4 1.5 0.4 setosa
17 │ 17 5.4 3.9 1.3 0.4 setosa
18 │ 18 5.1 3.5 1.4 0.3 setosa
19 │ 19 5.7 3.8 1.7 0.3 setosa
20 │ 20 5.1 3.8 1.5 0.3 setosa
21 │ 21 5.4 3.4 1.7 0.2 setosa
22 │ 22 5.1 3.7 1.5 0.4 setosa
23 │ 23 4.6 3.6 1.0 0.2 setosa
24 │ 24 5.1 3.3 1.7 0.5 setosa
25 │ 25 4.8 3.4 1.9 0.2 setosa
26 │ 26 5.0 3.0 1.6 0.2 setosa
27 │ 27 5.0 3.4 1.6 0.4 setosa
28 │ 28 5.2 3.5 1.5 0.2 setosa
29 │ 29 5.2 3.4 1.4 0.2 setosa
30 │ 30 4.7 3.2 1.6 0.2 setosa
31 │ 31 4.8 3.1 1.6 0.2 setosa
32 │ 32 5.4 3.4 1.5 0.4 setosa
33 │ 33 5.2 4.1 1.5 0.1 setosa
34 │ 34 5.5 4.2 1.4 0.2 setosa
35 │ 35 4.9 3.1 1.5 0.2 setosa
36 │ 36 5.0 3.2 1.2 0.2 setosa
37 │ 37 5.5 3.5 1.3 0.2 setosa
38 │ 38 4.9 3.6 1.4 0.1 setosa
39 │ 39 4.4 3.0 1.3 0.2 setosa
40 │ 40 5.1 3.4 1.5 0.2 setosa
41 │ 41 5.0 3.5 1.3 0.3 setosa
42 │ 42 4.5 2.3 1.3 0.3 setosa
43 │ 43 4.4 3.2 1.3 0.2 setosa
44 │ 44 5.0 3.5 1.6 0.6 setosa
45 │ 45 5.1 3.8 1.9 0.4 setosa
46 │ 46 4.8 3.0 1.4 0.3 setosa
47 │ 47 5.1 3.8 1.6 0.2 setosa
48 │ 48 4.6 3.2 1.4 0.2 setosa
49 │ 49 5.3 3.7 1.5 0.2 setosa
50 │ 50 5.0 3.3 1.4 0.2 setosa
51 │ 51 7.0 3.2 4.7 1.4 versicolor
52 │ 52 6.4 3.2 4.5 1.5 versicolor
53 │ 53 6.9 3.1 4.9 1.5 versicolor
54 │ 54 5.5 2.3 4.0 1.3 versicolor
55 │ 55 6.5 2.8 4.6 1.5 versicolor
56 │ 56 5.7 2.8 4.5 1.3 versicolor
57 │ 57 6.3 3.3 4.7 1.6 versicolor
58 │ 58 4.9 2.4 3.3 1.0 versicolor
59 │ 59 6.6 2.9 4.6 1.3 versicolor
60 │ 60 5.2 2.7 3.9 1.4 versicolor
61 │ 61 5.0 2.0 3.5 1.0 versicolor
62 │ 62 5.9 3.0 4.2 1.5 versicolor
63 │ 63 6.0 2.2 4.0 1.0 versicolor
64 │ 64 6.1 2.9 4.7 1.4 versicolor
65 │ 65 5.6 2.9 3.6 1.3 versicolor
66 │ 66 6.7 3.1 4.4 1.4 versicolor
67 │ 67 5.6 3.0 4.5 1.5 versicolor
68 │ 68 5.8 2.7 4.1 1.0 versicolor
69 │ 69 6.2 2.2 4.5 1.5 versicolor
70 │ 70 5.6 2.5 3.9 1.1 versicolor
71 │ 71 5.9 3.2 4.8 1.8 versicolor
72 │ 72 6.1 2.8 4.0 1.3 versicolor
73 │ 73 6.3 2.5 4.9 1.5 versicolor
74 │ 74 6.1 2.8 4.7 1.2 versicolor
75 │ 75 6.4 2.9 4.3 1.3 versicolor
76 │ 76 6.6 3.0 4.4 1.4 versicolor
77 │ 77 6.8 2.8 4.8 1.4 versicolor
78 │ 78 6.7 3.0 5.0 1.7 versicolor
79 │ 79 6.0 2.9 4.5 1.5 versicolor
80 │ 80 5.7 2.6 3.5 1.0 versicolor
81 │ 81 5.5 2.4 3.8 1.1 versicolor
82 │ 82 5.5 2.4 3.7 1.0 versicolor
83 │ 83 5.8 2.7 3.9 1.2 versicolor
84 │ 84 6.0 2.7 5.1 1.6 versicolor
85 │ 85 5.4 3.0 4.5 1.5 versicolor
86 │ 86 6.0 3.4 4.5 1.6 versicolor
87 │ 87 6.7 3.1 4.7 1.5 versicolor
88 │ 88 6.3 2.3 4.4 1.3 versicolor
89 │ 89 5.6 3.0 4.1 1.3 versicolor
90 │ 90 5.5 2.5 4.0 1.3 versicolor
91 │ 91 5.5 2.6 4.4 1.2 versicolor
92 │ 92 6.1 3.0 4.6 1.4 versicolor
93 │ 93 5.8 2.6 4.0 1.2 versicolor
94 │ 94 5.0 2.3 3.3 1.0 versicolor
95 │ 95 5.6 2.7 4.2 1.3 versicolor
96 │ 96 5.7 3.0 4.2 1.2 versicolor
97 │ 97 5.7 2.9 4.2 1.3 versicolor
98 │ 98 6.2 2.9 4.3 1.3 versicolor
99 │ 99 5.1 2.5 3.0 1.1 versicolor
100 │ 100 5.7 2.8 4.1 1.3 versicolor
101 │ 101 6.3 3.3 6.0 2.5 virginica
102 │ 102 5.8 2.7 5.1 1.9 virginica
103 │ 103 7.1 3.0 5.9 2.1 virginica
104 │ 104 6.3 2.9 5.6 1.8 virginica
105 │ 105 6.5 3.0 5.8 2.2 virginica
106 │ 106 7.6 3.0 6.6 2.1 virginica
107 │ 107 4.9 2.5 4.5 1.7 virginica
108 │ 108 7.3 2.9 6.3 1.8 virginica
109 │ 109 6.7 2.5 5.8 1.8 virginica
110 │ 110 7.2 3.6 6.1 2.5 virginica
111 │ 111 6.5 3.2 5.1 2.0 virginica
112 │ 112 6.4 2.7 5.3 1.9 virginica
113 │ 113 6.8 3.0 5.5 2.1 virginica
114 │ 114 5.7 2.5 5.0 2.0 virginica
115 │ 115 5.8 2.8 5.1 2.4 virginica
116 │ 116 6.4 3.2 5.3 2.3 virginica
117 │ 117 6.5 3.0 5.5 1.8 virginica
118 │ 118 7.7 3.8 6.7 2.2 virginica
119 │ 119 7.7 2.6 6.9 2.3 virginica
120 │ 120 6.0 2.2 5.0 1.5 virginica
121 │ 121 6.9 3.2 5.7 2.3 virginica
122 │ 122 5.6 2.8 4.9 2.0 virginica
123 │ 123 7.7 2.8 6.7 2.0 virginica
124 │ 124 6.3 2.7 4.9 1.8 virginica
125 │ 125 6.7 3.3 5.7 2.1 virginica
126 │ 126 7.2 3.2 6.0 1.8 virginica
127 │ 127 6.2 2.8 4.8 1.8 virginica
128 │ 128 6.1 3.0 4.9 1.8 virginica
129 │ 129 6.4 2.8 5.6 2.1 virginica
130 │ 130 7.2 3.0 5.8 1.6 virginica
131 │ 131 7.4 2.8 6.1 1.9 virginica
132 │ 132 7.9 3.8 6.4 2.0 virginica
133 │ 133 6.4 2.8 5.6 2.2 virginica
134 │ 134 6.3 2.8 5.1 1.5 virginica
135 │ 135 6.1 2.6 5.6 1.4 virginica
136 │ 136 7.7 3.0 6.1 2.3 virginica
137 │ 137 6.3 3.4 5.6 2.4 virginica
138 │ 138 6.4 3.1 5.5 1.8 virginica
139 │ 139 6.0 3.0 4.8 1.8 virginica
140 │ 140 6.9 3.1 5.4 2.1 virginica
141 │ 141 6.7 3.1 5.6 2.4 virginica
142 │ 142 6.9 3.1 5.1 2.3 virginica
143 │ 143 5.8 2.7 5.1 1.9 virginica
144 │ 144 6.8 3.2 5.9 2.3 virginica
145 │ 145 6.7 3.3 5.7 2.5 virginica
146 │ 146 6.7 3.0 5.2 2.3 virginica
147 │ 147 6.3 2.5 5.0 1.9 virginica
148 │ 148 6.5 3.0 5.2 2.0 virginica
149 │ 149 6.2 3.4 5.4 2.3 virginica
150 │ 150 5.9 3.0 5.1 1.8 virginica
3×4 DataFrame
Row │ species count slavg slsum
│ String Int64 Float64 Float64
─────┼─────────────────────────────────────
1 │ setosa 1800 5.006 9010.8
2 │ versicolor 1800 5.936 10684.8
3 │ virginica 1800 6.588 11858.4
SQLite.DBTable[SQLite.DBTable("iris", Tables.Schema:
:? Union{Missing, String}
:sepal_length Union{Missing, String}
:sepal_width Union{Missing, String}
:petal_length Union{Missing, String}
:petal_width Union{Missing, String}
:species Union{Missing, String}), SQLite.DBTable("iris_table", Tables.Schema:
:Column1 Union{Missing, Int64}
:sepal_length Union{Missing, Float64}
:sepal_width Union{Missing, Float64}
:petal_length Union{Missing, Float64}
:petal_width Union{Missing, Float64}
:species Union{Missing, String})]calling qt.html
calling plutohtml
calling riveropt.pdf
Python setup
sample
5.0 12.0 13.0import csv file using panda
sepal_length sepal_width petal_length petal_width species
0 5.1 3.5 1.4 0.2 setosa
1 4.9 3.0 1.4 0.2 NaN
2 4.7 3.2 1.3 0.2 setosa
3 NaN 3.1 1.5 0.2 setosa
4 5.0 3.6 NaN 0.2 NaN
.. ... ... ... ... ...
145 6.7 3.0 5.2 2.3 virginica
146 6.3 NaN 5.0 1.9 virginica
147 6.5 NaN 5.2 2.0 virginica
148 6.2 3.4 5.4 2.3 virginica
149 5.9 3.0 5.1 1.8 virginica
[150 rows x 5 columns]<class 'pandas.core.frame.DataFrame'>
RangeIndex: 150 entries, 0 to 149
Data columns (total 5 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 sepal_length 138 non-null float64
1 sepal_width 134 non-null float64
2 petal_length 138 non-null float64
3 petal_width 134 non-null float64
4 species 131 non-null object
dtypes: float64(4), object(1)
memory usage: 6.0+ KB sepal_length sepal_width petal_length petal_width
count 138.000000 134.000000 138.000000 134.000000
mean 5.786957 3.058955 3.821739 1.182090
std 0.789329 0.423770 1.776519 0.760871
min 4.300000 2.000000 1.000000 0.100000
25% 5.100000 2.800000 1.600000 0.300000
50% 5.700000 3.000000 4.400000 1.300000
75% 6.400000 3.300000 5.100000 1.800000
max 7.900000 4.400000 6.900000 2.5000001505array(['sepal_length', 'sepal_width', 'petal_length', 'petal_width',
'species'], dtype=object)how to remove missing values in python using pandas
sepal_length 12
sepal_width 16
petal_length 12
petal_width 16
species 19
dtype: int64 sepal_length sepal_width petal_length petal_width species
0 5.1 3.5 1.4 0.2 setosa
1 4.9 3.0 1.4 0.2 setosa
2 4.7 3.2 1.3 0.2 setosa
3 4.7 3.1 1.5 0.2 setosa
4 5.0 3.6 1.5 0.2 setosa
.. ... ... ... ... ...
145 6.7 3.0 5.2 2.3 virginica
146 6.3 3.0 5.0 1.9 virginica
147 6.5 3.0 5.2 2.0 virginica
148 6.2 3.4 5.4 2.3 virginica
149 5.9 3.0 5.1 1.8 virginica
[150 rows x 5 columns]sepal_length 0
sepal_width 0
petal_length 0
petal_width 0
species 0
dtype: int64