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glfw initialised
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########################
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: 0.9
ConfusionMatrix{3}([14 0 0; 0 17 4; 0 1 14])
[14 0 0; 0 17 4; 0 1 14]
############
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): 1.8944182288019402
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: 0.96
ConfusionMatrix{3}([14 0 0; 0 17 1; 0 1 17])
[14 0 0; 0 17 1; 0 1 17]
############
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.
[0.9666666666666667, 0.9, 0.9666666666666667, 0.9666666666666667, 0.9]
0.9400000000000001----0.036514837167011066
#############
mlj14 running
#############
All packages loaded successfully.
[0.9666666666666667, 1.0, 0.9, 0.8666666666666667, 1.0]
0.9466666666666667----0.060553007081949814
###########################
mlj16 with cross validation
###########################
[1.0, 0.95, 0.95, 1.0, 1.0]
0.9800000000000001
0.02738612787525833
#####################################
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 = [RootMeanSquaredError()], measurement = [4.621983720432108], per_fold = [[3.1498851519679625, 3.307942437273786, 5.265947866309833, 6.583592191092477, 3.856878900287789]]), history = @NamedTuple{model::MLJDecisionTreeInterface.RandomForestRegressor, measure::Vector{MLJBase.RootMeanSquaredError}, measurement::Vector{Float64}, per_fold::Vector{Vector{Float64}}}[(model = RandomForestRegressor(max_depth = -1, …), measure = [RootMeanSquaredError()], measurement = [5.021924787822756], per_fold = [[3.3741287546478103, 4.103380639578459, 4.568646619152445, 6.897883607841374, 5.424281993823224]]), (model = RandomForestRegressor(max_depth = -1, …), measure = [RootMeanSquaredError()], measurement = [4.6774372882045085], per_fold = [[3.003770053048402, 3.9444903644734066, 4.693197597093959, 6.789751223142656, 4.084560417092651]]), (model = RandomForestRegressor(max_depth = -1, …), measure = [RootMeanSquaredError()], measurement = [4.772483574701619], per_fold = [[2.9948640131926414, 3.9887668115821007, 4.959489325759872, 6.674681289880182, 4.455964713222702]]), (model = RandomForestRegressor(max_depth = -1, …), measure = [RootMeanSquaredError()], measurement = [4.703944145986171], per_fold = [[3.04157986553557, 3.9504312360165246, 4.642152478502118, 6.7973135687913695, 4.245619651796751]]), (model = RandomForestRegressor(max_depth = -1, …), measure = [RootMeanSquaredError()], measurement = [4.675715674831727], per_fold = [[3.0668645869369326, 3.9048956622481676, 4.652229167983413, 6.541823741045831, 4.496556765052191]]), (model = RandomForestRegressor(max_depth = -1, …), measure = [RootMeanSquaredError()], measurement = [4.7051750843571725], per_fold = [[2.936317439249649, 3.8544894797266087, 4.801542464920288, 6.611152096789043, 4.522407713773929]]), (model = RandomForestRegressor(max_depth = -1, …), measure = [RootMeanSquaredError()], measurement = [4.707833964624099], per_fold = [[3.1468033499933568, 3.855049055177091, 4.498632912528334, 7.071324837883725, 3.9766060411405664]]), (model = RandomForestRegressor(max_depth = -1, …), measure = [RootMeanSquaredError()], measurement = [4.765659732014386], per_fold = [[3.1668394215003435, 3.9383442316700448, 4.8052750099023545, 6.669651078201205, 4.521418225764702]]), (model = RandomForestRegressor(max_depth = -1, …), measure = [RootMeanSquaredError()], measurement = [4.621983720432108], per_fold = [[3.1498851519679625, 3.307942437273786, 5.265947866309833, 6.583592191092477, 3.856878900287789]]), (model = RandomForestRegressor(max_depth = -1, …), measure = [RootMeanSquaredError()], measurement = [4.844822691796512], per_fold = [[2.9870176796825936, 3.6120895128809996, 4.852258918448313, 7.0890030269701265, 4.646900986558451]])], 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[20; 60; 90; 100; 80; 70; 40; 50; 10; 30;;], measurements = [5.021924787822756, 4.6774372882045085, 4.772483574701619, 4.703944145986171, 4.675715674831727, 4.7051750843571725, 4.707833964624099, 4.765659732014386, 4.621983720432108, 4.844822691796512]))
---------------
(best_model = RandomForestRegressor(max_depth = -1, …), best_fitted_params = (forest = Ensemble of Decision Trees
Trees: 10
Avg Leaves: 244.8
Avg Depth: 18.1,))
---------------
[4.621983720432108]
---------------
(model = RandomForestRegressor(max_depth = -1, …), measure = [RootMeanSquaredError()], measurement = [4.621983720432108], per_fold = [[3.1498851519679625, 3.307942437273786, 5.265947866309833, 6.583592191092477, 3.856878900287789]])
---------------
@NamedTuple{model::MLJDecisionTreeInterface.RandomForestRegressor, measure::Vector{MLJBase.RootMeanSquaredError}, measurement::Vector{Float64}, per_fold::Vector{Vector{Float64}}}[(model = RandomForestRegressor(max_depth = -1, …), measure = [RootMeanSquaredError()], measurement = [5.021924787822756], per_fold = [[3.3741287546478103, 4.103380639578459, 4.568646619152445, 6.897883607841374, 5.424281993823224]]), (model = RandomForestRegressor(max_depth = -1, …), measure = [RootMeanSquaredError()], measurement = [4.6774372882045085], per_fold = [[3.003770053048402, 3.9444903644734066, 4.693197597093959, 6.789751223142656, 4.084560417092651]]), (model = RandomForestRegressor(max_depth = -1, …), measure = [RootMeanSquaredError()], measurement = [4.772483574701619], per_fold = [[2.9948640131926414, 3.9887668115821007, 4.959489325759872, 6.674681289880182, 4.455964713222702]]), (model = RandomForestRegressor(max_depth = -1, …), measure = [RootMeanSquaredError()], measurement = [4.703944145986171], per_fold = [[3.04157986553557, 3.9504312360165246, 4.642152478502118, 6.7973135687913695, 4.245619651796751]]), (model = RandomForestRegressor(max_depth = -1, …), measure = [RootMeanSquaredError()], measurement = [4.675715674831727], per_fold = [[3.0668645869369326, 3.9048956622481676, 4.652229167983413, 6.541823741045831, 4.496556765052191]]), (model = RandomForestRegressor(max_depth = -1, …), measure = [RootMeanSquaredError()], measurement = [4.7051750843571725], per_fold = [[2.936317439249649, 3.8544894797266087, 4.801542464920288, 6.611152096789043, 4.522407713773929]]), (model = RandomForestRegressor(max_depth = -1, …), measure = [RootMeanSquaredError()], measurement = [4.707833964624099], per_fold = [[3.1468033499933568, 3.855049055177091, 4.498632912528334, 7.071324837883725, 3.9766060411405664]]), (model = RandomForestRegressor(max_depth = -1, …), measure = [RootMeanSquaredError()], measurement = [4.765659732014386], per_fold = [[3.1668394215003435, 3.9383442316700448, 4.8052750099023545, 6.669651078201205, 4.521418225764702]]), (model = RandomForestRegressor(max_depth = -1, …), measure = [RootMeanSquaredError()], measurement = [4.621983720432108], per_fold = [[3.1498851519679625, 3.307942437273786, 5.265947866309833, 6.583592191092477, 3.856878900287789]]), (model = RandomForestRegressor(max_depth = -1, …), measure = [RootMeanSquaredError()], measurement = [4.844822691796512], per_fold = [[2.9870176796825936, 3.6120895128809996, 4.852258918448313, 7.0890030269701265, 4.646900986558451]])]
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 main