Classwork
Jarvis Woodard
2024-05-14
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set.seed(1234)
randDat <- matrix(rnorm(50), nrow = 5)
dist(randDat) # Euclidean distance (default)## 1 2 3 4
## 2 4.261667
## 3 4.038030 2.060117
## 4 3.456732 3.726399 4.037978
## 5 5.307253 4.415046 4.111230 4.814393dist(randDat, method = 'manhattan')## 1 2 3 4
## 2 11.382197
## 3 10.016795 4.536827
## 4 9.887932 8.845512 8.829131
## 5 14.683770 10.617871 9.091241 11.362705dist(randDat, method = 'minkowski', p= 4)## 1 2 3 4
## 2 2.899494
## 3 2.875467 1.653824
## 4 2.208297 2.814135 3.453336
## 5 3.488531 3.192217 3.398721 3.643788randDat## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] -1.2070657 0.5060559 -0.47719270 -0.1102855 0.1340882 -1.4482049
## [2,] 0.2774292 -0.5747400 -0.99838644 -0.5110095 -0.4906859 0.5747557
## [3,] 1.0844412 -0.5466319 -0.77625389 -0.9111954 -0.4405479 -1.0236557
## [4,] -2.3456977 -0.5644520 0.06445882 -0.8371717 0.4595894 -0.0151383
## [5,] 0.4291247 -0.8900378 0.95949406 2.4158352 -0.6937202 -0.9359486
## [,7] [,8] [,9] [,10]
## [1,] 1.1022975 -1.1676193 1.4494963 -0.9685143
## [2,] -0.4755931 -2.1800396 -1.0686427 -1.1073182
## [3,] -0.7094400 -1.3409932 -0.8553646 -1.2519859
## [4,] -0.5012581 -0.2942939 -0.2806230 -0.5238281
## [5,] -1.6290935 -0.4658975 -0.9943401 -0.4968500library(readr)## Warning: package 'readr' was built under R version 4.3.2schoolExpenditure <- read_csv("C:/Users/Woodard2606/Downloads/schoolExpenditure.csv")## Rows: 220 Columns: 14
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (14): expreg, expspecial, expbil, expocc, exptot, scratio, special, lunc...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.View(schoolExpenditure)
d<-dist(scale(schoolExpenditure))
d_df <- as.data.frame(as.matrix(d))
View(d_df)
summary(d)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.5461 3.1472 4.2241 4.7402 5.7085 21.1348h<-hclust(d)
plot(h,hang = -0.1,labels=schoolExpenditure[['espreg']],cex=0.5)
c<- cutree(h,3)
c## [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
## [38] 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
## [75] 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
## [112] 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
## [149] 1 1 3 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
## [186] 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(cm<-table(c,schoolExpenditure$expreg))##
## c 2905 2956 3023 3079 3149 3287 3326 3333 3337 3400 3407 3465 3522 3533 3555
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##
## c 3580 3614 3627 3647 3679 3687 3693 3703 3704 3744 3747 3748 3795 3802 3823
## 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 2
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##
## c 3839 3845 3850 3858 3862 3867 3903 3905 3906 3908 3913 3928 3935 3969 3979
## 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##
## c 3986 4009 4013 4015 4016 4034 4062 4066 4071 4078 4079 4100 4105 4106 4122
## 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##
## c 4129 4134 4138 4150 4166 4177 4189 4201 4204 4205 4221 4234 4239 4240 4242
## 1 1 1 1 1 1 1 1 1 1 2 2 1 0 1 1
## 2 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##
## c 4247 4254 4255 4263 4267 4271 4273 4283 4287 4297 4312 4328 4344 4389 4398
## 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##
## c 4411 4413 4415 4436 4439 4440 4442 4447 4464 4465 4472 4484 4493 4494 4500
## 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##
## c 4506 4511 4512 4521 4529 4537 4539 4545 4557 4558 4562 4564 4566 4578 4579
## 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##
## c 4586 4598 4606 4609 4611 4617 4623 4625 4638 4640 4643 4660 4682 4685 4686
## 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##
## c 4707 4716 4754 4756 4764 4777 4793 4810 4821 4825 4832 4836 4859 4899 4922
## 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##
## c 4932 4945 4961 5004 5006 5009 5014 5048 5066 5087 5107 5146 5152 5183 5196
## 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##
## c 5242 5254 5273 5304 5323 5338 5345 5372 5384 5387 5396 5455 5457 5518 5548
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##
## c 5556 5558 5608 5613 5631 5690 5764 5770 5859 5896 5937 5978 6049 6152 6158
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
##
## c 6240 6277 6337 6370 6415 6554 6672 6881 6902 7763 7944 8759
## 1 1 1 1 1 1 1 2 1 1 1 1 1
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0cm##
## c 2905 2956 3023 3079 3149 3287 3326 3333 3337 3400 3407 3465 3522 3533 3555
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##
## c 3580 3614 3627 3647 3679 3687 3693 3703 3704 3744 3747 3748 3795 3802 3823
## 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 2
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##
## c 3839 3845 3850 3858 3862 3867 3903 3905 3906 3908 3913 3928 3935 3969 3979
## 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##
## c 3986 4009 4013 4015 4016 4034 4062 4066 4071 4078 4079 4100 4105 4106 4122
## 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##
## c 4129 4134 4138 4150 4166 4177 4189 4201 4204 4205 4221 4234 4239 4240 4242
## 1 1 1 1 1 1 1 1 1 1 2 2 1 0 1 1
## 2 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##
## c 4247 4254 4255 4263 4267 4271 4273 4283 4287 4297 4312 4328 4344 4389 4398
## 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##
## c 4411 4413 4415 4436 4439 4440 4442 4447 4464 4465 4472 4484 4493 4494 4500
## 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##
## c 4506 4511 4512 4521 4529 4537 4539 4545 4557 4558 4562 4564 4566 4578 4579
## 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##
## c 4586 4598 4606 4609 4611 4617 4623 4625 4638 4640 4643 4660 4682 4685 4686
## 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##
## c 4707 4716 4754 4756 4764 4777 4793 4810 4821 4825 4832 4836 4859 4899 4922
## 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##
## c 4932 4945 4961 5004 5006 5009 5014 5048 5066 5087 5107 5146 5152 5183 5196
## 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##
## c 5242 5254 5273 5304 5323 5338 5345 5372 5384 5387 5396 5455 5457 5518 5548
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##
## c 5556 5558 5608 5613 5631 5690 5764 5770 5859 5896 5937 5978 6049 6152 6158
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
##
## c 6240 6277 6337 6370 6415 6554 6672 6881 6902 7763 7944 8759
## 1 1 1 1 1 1 1 2 1 1 1 1 1
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0library(cluster)
a<-silhouette(c,d)
plot(a)
d<-dist(scale(schoolExpenditure))
methds<-c('complete','single','average')
avgS<-matrix(NA,ncol=3,nrow=5,dimnames=list(2:6,methds))
for (k in 2:6){
for(m in seq_along(methds)){
h<-hclust(d,meth=methds[m])
c<-cutree(h,k)
s<-silhouette(c,d)
avgS[k-1,m]=mean(s[,3])
}
}
h##
## Call:
## hclust(d = d, method = methds[m])
##
## Cluster method : average
## Distance : euclidean
## Number of objects: 220c## [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 1 1 3
## [38] 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
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 4 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 5 1 1 1 1 1 1 1
## [112] 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
## [149] 1 1 6 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
## [186] 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 5 1 1 1 1 1 1 1 1 1 1 1 1avgS## complete single average
## 2 0.6948343 0.6948343 0.6948343
## 3 0.6732931 0.6732931 0.6732931
## 4 0.4013864 0.4864268 0.5655894
## 5 0.2652299 0.4577155 0.4129798
## 6 0.2587648 0.4614335 0.3666606summary(d)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.5461 3.1472 4.2241 4.7402 5.7085 21.1348library(readr)
sdf <- read_table("C:/Users/Woodard2606/Downloads/seeds_dataset.txt")##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## `15.26` = col_double(),
## `14.84` = col_double(),
## `0.871` = col_double(),
## `5.763` = col_double(),
## `3.312` = col_double(),
## `2.221` = col_double(),
## `5.22` = col_double(),
## `1` = col_double()
## )View(sdf)
feature_name<-c('area','perimeter','compactness','length_of_kernal','width.of.kernal','asymetry.coefficient','LKG','type.of.seed')
View(sdf)
colnames(sdf)<-feature_name
str(sdf)## spc_tbl_ [209 × 8] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ area : num [1:209] 14.9 14.3 13.8 16.1 14.4 ...
## $ perimeter : num [1:209] 14.6 14.1 13.9 15 14.2 ...
## $ compactness : num [1:209] 0.881 0.905 0.895 0.903 0.895 ...
## $ length_of_kernal : num [1:209] 5.55 5.29 5.32 5.66 5.39 ...
## $ width.of.kernal : num [1:209] 3.33 3.34 3.38 3.56 3.31 ...
## $ asymetry.coefficient: num [1:209] 1.02 2.7 2.26 1.35 2.46 ...
## $ LKG : num [1:209] 4.96 4.83 4.8 5.17 4.96 ...
## $ type.of.seed : num [1:209] 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "spec")=
## .. cols(
## .. `15.26` = col_double(),
## .. `14.84` = col_double(),
## .. `0.871` = col_double(),
## .. `5.763` = col_double(),
## .. `3.312` = col_double(),
## .. `2.221` = col_double(),
## .. `5.22` = col_double(),
## .. `1` = col_double()
## .. )summary(sdf)## area perimeter compactness length_of_kernal
## Min. :10.59 Min. :12.41 Min. :0.8081 Min. :4.899
## 1st Qu.:12.26 1st Qu.:13.45 1st Qu.:0.8567 1st Qu.:5.262
## Median :14.34 Median :14.29 Median :0.8735 Median :5.520
## Mean :14.85 Mean :14.56 Mean :0.8710 Mean :5.628
## 3rd Qu.:17.32 3rd Qu.:15.73 3rd Qu.:0.8879 3rd Qu.:5.980
## Max. :21.18 Max. :17.25 Max. :0.9183 Max. :6.675
## width.of.kernal asymetry.coefficient LKG type.of.seed
## Min. :2.630 Min. :0.7651 Min. :4.519 Min. :1.000
## 1st Qu.:2.941 1st Qu.:2.5870 1st Qu.:5.045 1st Qu.:1.000
## Median :3.232 Median :3.6000 Median :5.224 Median :2.000
## Mean :3.258 Mean :3.7073 Mean :5.409 Mean :2.005
## 3rd Qu.:3.562 3rd Qu.:4.7730 3rd Qu.:5.877 3rd Qu.:3.000
## Max. :4.033 Max. :8.4560 Max. :6.550 Max. :3.000any(is.na(sdf))## [1] FALSEseeds_df<-na.omit(sdf)
seeds_label <- seeds_df$type.of.seed
seeds_df$type.of.seed <- NULL
str(sdf)## spc_tbl_ [209 × 8] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ area : num [1:209] 14.9 14.3 13.8 16.1 14.4 ...
## $ perimeter : num [1:209] 14.6 14.1 13.9 15 14.2 ...
## $ compactness : num [1:209] 0.881 0.905 0.895 0.903 0.895 ...
## $ length_of_kernal : num [1:209] 5.55 5.29 5.32 5.66 5.39 ...
## $ width.of.kernal : num [1:209] 3.33 3.34 3.38 3.56 3.31 ...
## $ asymetry.coefficient: num [1:209] 1.02 2.7 2.26 1.35 2.46 ...
## $ LKG : num [1:209] 4.96 4.83 4.8 5.17 4.96 ...
## $ type.of.seed : num [1:209] 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "spec")=
## .. cols(
## .. `15.26` = col_double(),
## .. `14.84` = col_double(),
## .. `0.871` = col_double(),
## .. `5.763` = col_double(),
## .. `3.312` = col_double(),
## .. `2.221` = col_double(),
## .. `5.22` = col_double(),
## .. `1` = col_double()
## .. )seeds_df_sc <- as.data.frame(scale(sdf))
summary(seeds_df_sc)## area perimeter compactness length_of_kernal
## Min. :-1.4591 Min. :-1.6410 Min. :-2.6555 Min. :-1.642
## 1st Qu.:-0.8865 1st Qu.:-0.8464 1st Qu.:-0.6037 1st Qu.:-0.824
## Median :-0.1733 Median :-0.2047 Median : 0.1056 Median :-0.243
## Mean : 0.0000 Mean : 0.0000 Mean : 0.0000 Mean : 0.000
## 3rd Qu.: 0.8484 3rd Qu.: 0.8954 3rd Qu.: 0.7136 3rd Qu.: 0.793
## Max. : 2.1719 Max. : 2.0567 Max. : 1.9970 Max. : 2.358
## width.of.kernal asymetry.coefficient LKG type.of.seed
## Min. :-1.6597 Min. :-1.95668 Min. :-1.8071 Min. :-1.229150
## 1st Qu.:-0.8382 1st Qu.:-0.74504 1st Qu.:-0.7390 1st Qu.:-1.229150
## Median :-0.0696 Median :-0.07135 Median :-0.3756 Median :-0.005853
## Mean : 0.0000 Mean : 0.00000 Mean : 0.0000 Mean : 0.000000
## 3rd Qu.: 0.8020 3rd Qu.: 0.70875 3rd Qu.: 0.9503 3rd Qu.: 1.217443
## Max. : 2.0461 Max. : 3.15812 Max. : 2.3169 Max. : 1.217443dist_mat <- dist(seeds_df_sc, method = 'euclidean')
hclust_avg<- hclust(dist_mat,method = 'average')
plot(hclust_avg)
cut_avg <- cutree(hclust_avg, k = 3)
#plot(hclust_avg)
#rect.hclust(hclust_avg , k = 3,border = 2:6)
#abline(h=3,col='red')
#install.packages("dendextend")
library(dendextend)## Warning: package 'dendextend' was built under R version 4.3.3##
## ---------------------
## Welcome to dendextend version 1.17.1
## Type citation('dendextend') for how to cite the package.
##
## Type browseVignettes(package = 'dendextend') for the package vignette.
## The github page is: https://github.com/talgalili/dendextend/
##
## Suggestions and bug-reports can be submitted at: https://github.com/talgalili/dendextend/issues
## You may ask questions at stackoverflow, use the r and dendextend tags:
## https://stackoverflow.com/questions/tagged/dendextend
##
## To suppress this message use: suppressPackageStartupMessages(library(dendextend))
## ---------------------
##
##
## Attaching package: 'dendextend'
##
## The following object is masked from 'package:stats':
##
## cutree
avg_dend_obj <-as.dendrogram(hclust_avg)
avg_col_dend <-color_branches(avg_dend_obj, h = 3)
plot(avg_col_dend)
library(dplyr)## Warning: package 'dplyr' was built under R version 4.3.2##
## Attaching package: 'dplyr'
##
## The following objects are masked from 'package:stats':
##
## filter, lag
##
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionseeds_df_cl <- mutate(sdf, cluster = cut_avg)
count(seeds_df_cl,cluster)## # A tibble: 3 × 2
## cluster n
## <int> <int>
## 1 1 71
## 2 2 68
## 3 3 70library(ggplot2)## Warning: package 'ggplot2' was built under R version 4.3.3ggplot(seeds_df_cl, aes(x=area, y = perimeter, color = factor(cluster))) +geom_point()
library("clValid")## Warning: package 'clValid' was built under R version 4.3.3dunn(dist_mat,cut_avg)## [1] 0.2247805set.seed(1234)
data(iris)
ir3<- kmeans(iris[,-5],center=3,iter.max = 200)
ir3## K-means clustering with 3 clusters of sizes 50, 62, 38
##
## Cluster means:
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## 1 5.006000 3.428000 1.462000 0.246000
## 2 5.901613 2.748387 4.393548 1.433871
## 3 6.850000 3.073684 5.742105 2.071053
##
## Clustering vector:
## [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
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [75] 2 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 3 3 3 3 2 3 3 3 3
## [112] 3 3 2 2 3 3 3 3 2 3 2 3 2 3 3 2 2 3 3 3 3 3 2 3 3 3 3 2 3 3 3 2 3 3 3 2 3
## [149] 3 2
##
## Within cluster sum of squares by cluster:
## [1] 15.15100 39.82097 23.87947
## (between_SS / total_SS = 88.4 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
## [6] "betweenss" "size" "iter" "ifault"table(ir3$cluster,iris$Species)##
## setosa versicolor virginica
## 1 50 0 0
## 2 0 48 14
## 3 0 2 36#cm<-table(iris$cluster,iris$Species)
1-sum(diag(cm))/sum(cm)## [1] 0.9954545train<-data.frame(ClaimID= c(1,2,3),
RearEnd = c(TRUE,FALSE,TRUE), Fraud = c(TRUE,FALSE,FALSE) )
train## ClaimID RearEnd Fraud
## 1 1 TRUE TRUE
## 2 2 FALSE FALSE
## 3 3 TRUE FALSElibrary(rpart)## Warning: package 'rpart' was built under R version 4.3.3##
## Attaching package: 'rpart'
##
## The following object is masked from 'package:dendextend':
##
## prunemytree<-rpart(Fraud~RearEnd,data =train,method= "class")
mytree## n= 3
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 3 1 FALSE (0.6666667 0.3333333) *nytree<-rpart(Fraud ~ RearEnd,data = train,
method = "class",minsplit = 2, minbucket = 1,cp= -1)
nytree## n= 3
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 3 1 FALSE (0.6666667 0.3333333)
## 2) RearEnd< 0.5 1 0 FALSE (1.0000000 0.0000000) *
## 3) RearEnd>=0.5 2 1 FALSE (0.5000000 0.5000000) *library(rattle)## Warning: package 'rattle' was built under R version 4.3.3## Loading required package: tibble## Warning: package 'tibble' was built under R version 4.3.2## Loading required package: bitops
## Rattle: A free graphical interface for data science with R.
## Version 5.5.1 Copyright (c) 2006-2021 Togaware Pty Ltd.
## Type 'rattle()' to shake, rattle, and roll your data.library(rpart.plot)## Warning: package 'rpart.plot' was built under R version 4.3.3library(RColorBrewer)
fancyRpartPlot(nytree,caption = NULL)
nytree<-rpart(Fraud ~ RearEnd, data = train, method = "class",parms= list(split ='information'),minsplit = 2)
nytree## n= 3
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 3 1 FALSE (0.6666667 0.3333333) *#fancyRpartPlot(nytree,caption = NULL)
train <- data.frame(ClaimID = 1:7, RearEnd = c(TRUE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE),Whiplash = c(TRUE,TRUE,TRUE,TRUE,TRUE,FALSE,FALSE),Fraud = c(TRUE,TRUE,TRUE,FALSE,FALSE,FALSE,FALSE))
train## ClaimID RearEnd Whiplash Fraud
## 1 1 TRUE TRUE TRUE
## 2 2 TRUE TRUE TRUE
## 3 3 FALSE TRUE TRUE
## 4 4 FALSE TRUE FALSE
## 5 5 FALSE TRUE FALSE
## 6 6 FALSE FALSE FALSE
## 7 7 FALSE FALSE FALSEnytree<-rpart(Fraud ~ RearEnd+Whiplash,data = train,
method = "class",minsplit = 2, minbucket = 1)
fancyRpartPlot(nytree,caption = NULL)
lossmatrix <-matrix(c(0,1,3,0),byrow =TRUE,nrow =2)
lossmatrix## [,1] [,2]
## [1,] 0 1
## [2,] 3 0nytree<-rpart(Fraud ~ RearEnd+Whiplash,data = train,
method = "class",maxdepth = 1,minsplit = 2, minbucket = 1,parms = list(loss = lossmatrix))
nytree## n= 7
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 7 4 TRUE (0.5714286 0.4285714)
## 2) Whiplash< 0.5 2 0 FALSE (1.0000000 0.0000000) *
## 3) Whiplash>=0.5 5 2 TRUE (0.4000000 0.6000000) *fancyRpartPlot(nytree,caption = NULL)
train <- data.frame(ClaimID = c(1,2,3,4,5),Activity = factor(x = c("active","very active","very active","inactive","very inactive"),levels = c("very inactive","inactive","active","very active"),order = TRUE),Fraud = c(FALSE,TRUE,TRUE,FALSE,TRUE))
train## ClaimID Activity Fraud
## 1 1 active FALSE
## 2 2 very active TRUE
## 3 3 very active TRUE
## 4 4 inactive FALSE
## 5 5 very inactive TRUEnytree<-rpart(Fraud ~ Activity,data = train,
method = "class",minsplit = 2, minbucket = 1)
fancyRpartPlot(nytree,caption = NULL)
train <- data.frame(ClaimId = 1:10,RearEnd= c(TRUE,TRUE,TRUE,FALSE,FALSE,FALSE,FALSE,TRUE,TRUE,FALSE),Whiplash = c(TRUE,TRUE,TRUE,TRUE,TRUE,FALSE,FALSE,FALSE,FALSE,TRUE),Activity = factor(x = c("active","very active","very active","inactive","very inactive","inactive","very inactive","active","active","very active"),levels = c("very inactive","inactive","active","very active"),ordered = TRUE),Fraud = c(FALSE,TRUE,TRUE,FALSE,FALSE,TRUE,TRUE,FALSE,FALSE,TRUE))
nytree<-rpart(Fraud ~ RearEnd+Whiplash+Activity,data = train,
method = "class",minsplit = 2, minbucket = 1, cp = -1)
fancyRpartPlot(nytree,caption = NULL)
nytree$variable.importance## Activity Whiplash RearEnd
## 3.0000000 2.0000000 0.8571429names(nytree)## [1] "frame" "where" "call"
## [4] "terms" "cptable" "method"
## [7] "parms" "control" "functions"
## [10] "numresp" "splits" "csplit"
## [13] "variable.importance" "y" "ordered"nytree$method## [1] "class"print(nytree$method)## [1] "class"printcp(nytree)##
## Classification tree:
## rpart(formula = Fraud ~ RearEnd + Whiplash + Activity, data = train,
## method = "class", minsplit = 2, minbucket = 1, cp = -1)
##
## Variables actually used in tree construction:
## [1] Activity RearEnd Whiplash
##
## Root node error: 5/10 = 0.5
##
## n= 10
##
## CP nsplit rel error xerror xstd
## 1 0.6 0 1.0 2.0 0.00000
## 2 0.2 1 0.4 0.4 0.25298
## 3 -1.0 3 0.0 0.4 0.25298mytree <-prune(nytree,cp = 0.21)
fancyRpartPlot((nytree))
library(e1071)## Warning: package 'e1071' was built under R version 4.3.3data(iris)
set.seed(1234)
rndSample<-sample(1:nrow(iris),100)
tr<-iris[rndSample,]
ts<-iris[-rndSample,]
s<-svm(Species~.,tr)
ps<-predict(s,ts)
(cm<-table(ps,ts$Species))##
## ps setosa versicolor virginica
## setosa 18 0 0
## versicolor 0 17 1
## virginica 0 1 13precision<-100*(1-sum(diag(cm))/sum(cm))
precision## [1] 4s2<-svm(Species~.,tr, cost = 30, kernal = "polynomial",degree = 2)
ps2<-predict(s2,ts)
cm2<-table(ps2,ts$Species)
cm2##
## ps2 setosa versicolor virginica
## setosa 18 0 0
## versicolor 0 17 1
## virginica 0 1 13precision<-100*(1-sum(diag(cm2))/sum(cm2))
precision## [1] 4#the error rate
data(Boston,package = 'MASS')
set.seed(1234)
sp<-sample(1:nrow(Boston),354)
tr<-Boston[sp, ]
ts<-Boston[-sp, ]
s1<-svm(medv~.,tr)
ps1<-predict(s1,ts)
mean(abs(ps1-ts$medv))## [1] 2.158999s2<-svm(medv~.,tr, kernal = "radial",cost = 10, epsilon = 0.02,gamma=0.01)
ps2<-predict(s2,ts)
mean(abs(ps2-ts$medv))## [1] 2.124037library(readr)
letterdata <- read_csv("C:/Users/Woodard2606/Downloads/letterdata.csv")## Rows: 20000 Columns: 17
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): letter
## dbl (16): xbox, ybox, width, height, onpix, xbar, ybar, x2bar, y2bar, xybar,...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.View(letterdata)
library(kernlab)##
## Attaching package: 'kernlab'
##
## The following object is masked from 'package:ggplot2':
##
## alphaletters<-letterdata
letters$letter<-as.factor(letters$letter)
str(letters)## spc_tbl_ [20,000 × 17] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ letter: Factor w/ 26 levels "A","B","C","D",..: 20 9 4 14 7 19 2 1 10 13 ...
## $ xbox : num [1:20000] 2 5 4 7 2 4 4 1 2 11 ...
## $ ybox : num [1:20000] 8 12 11 11 1 11 2 1 2 15 ...
## $ width : num [1:20000] 3 3 6 6 3 5 5 3 4 13 ...
## $ height: num [1:20000] 5 7 8 6 1 8 4 2 4 9 ...
## $ onpix : num [1:20000] 1 2 6 3 1 3 4 1 2 7 ...
## $ xbar : num [1:20000] 8 10 10 5 8 8 8 8 10 13 ...
## $ ybar : num [1:20000] 13 5 6 9 6 8 7 2 6 2 ...
## $ x2bar : num [1:20000] 0 5 2 4 6 6 6 2 2 6 ...
## $ y2bar : num [1:20000] 6 4 6 6 6 9 6 2 6 2 ...
## $ xybar : num [1:20000] 6 13 10 4 6 5 7 8 12 12 ...
## $ x2ybar: num [1:20000] 10 3 3 4 5 6 6 2 4 1 ...
## $ xy2bar: num [1:20000] 8 9 7 10 9 6 6 8 8 9 ...
## $ xedge : num [1:20000] 0 2 3 6 1 0 2 1 1 8 ...
## $ xedgey: num [1:20000] 8 8 7 10 7 8 8 6 6 1 ...
## $ yedge : num [1:20000] 0 4 3 2 5 9 7 2 1 1 ...
## $ yedgex: num [1:20000] 8 10 9 8 10 7 10 7 7 8 ...
## - attr(*, "spec")=
## .. cols(
## .. letter = col_character(),
## .. xbox = col_double(),
## .. ybox = col_double(),
## .. width = col_double(),
## .. height = col_double(),
## .. onpix = col_double(),
## .. xbar = col_double(),
## .. ybar = col_double(),
## .. x2bar = col_double(),
## .. y2bar = col_double(),
## .. xybar = col_double(),
## .. x2ybar = col_double(),
## .. xy2bar = col_double(),
## .. xedge = col_double(),
## .. xedgey = col_double(),
## .. yedge = col_double(),
## .. yedgex = col_double()
## .. )
## - attr(*, "problems")=<externalptr>letters_train<-letters[1:16000,]
letters_test<-letters[16001:20000,]
letter_classifier<-ksvm(letter~.,data=letters_train,kernel= "vanilladot")## Setting default kernel parametersletter_predictions<-predict(letter_classifier, letters_test)
letter_predictions## [1] U N V X N H E Y G E N B G L G W M D Y R P D E W D S R G V R D J C I T C N
## [38] M B U R A A Q Q H K G S A J G W N H L B E Y J N S Z Q D T L G P K W H W A
## [75] H T I F I D T R Z I D F C S C B B N E D U L P C N F I Z F D G B Z T Q V E
## [112] A W K Y K L Q B B F S Q L T P A K F D Q L G C N K I P R O X V D A R Y K G
## [149] X E T D I I W X B I H F Z J M N A R J Z N K P I N W W G G V N G P Y J X L
## [186] G V Q Z D H K T U B W E R F K H H W L S R F U X P K U P K W W M U M J I C
## [223] M P B D U Z Q A Q W B B W U X W O V V U G O A J I Y E G K V Y L P L I O O
## [260] K Z Q B M J N A M R E G K E Q D L W J T Y F G J T Q J L N E Z Q O U Q O F
## [297] J L R H M F G Q E Q G L H A D Z U B J X G Z M M Q V F X I D L N H R S A O
## [334] S H R B R S O I K O C E A S A P N K X E D D A G J H S V Y E Y Y I T Q A U
## [371] B U C X Z D G D A O H K B R D Z W J X E H M B D W N X S H P G I J K R U K
## [408] N A F H Z X Z W P D E L K C Z Y X I A H A V P N S J Q U M D Z Y D J V W J
## [445] V G H D D M N G I P V E F J Q E B G Q I K G E Y O K N X J A Z R E D F O A
## [482] F L N F Y K W U A V U X K Y T S G Q L A R F P I O W D O V S O O R S L O Z
## [519] F P N T F M S X S X D L I Z Q F A E V S J X C L T R L Y E S E P T T V J H
## [556] C U F F R X E K O E K Y L K T J S A F U Y J A R G K T O P G R L U M R R D
## [593] G J Z D N S B H I A U G I G H W B A W U Q I F P E M C V D J L J F J G S Z
## [630] H Z O Q U M M X V V D F X L K J H D C C P Y L B L S O D L I D Y J U D D K
## [667] J R H A D A N U B R G N W G L U I Y P Z U U G L I M R N L W E D J Y D B B
## [704] D S T H A V A Z A R W Y L C U Y E R C L E M N Z P J S H B I D Q B M T V L
## [741] L Q Y T G Q F H V M G U S Z U W H T H V T D D R E J Q K N D H A L I R G R
## [778] X D R F C O V M R H H W Q Y G D A S P D Y B W O C I P Q A D S I L M V N G
## [815] V I T B V R H Q R P Y F Z O W C R Y I V Q G M Q P Z S D W A C Q R L D F U
## [852] Z S D Z C R A U R J Y E J M S U L R D X V H N M L H Z F Q M Y O E D O C X
## [889] L T B M U E J O T Y M Q S J A H M Y C J R Y M X J H H G K J V D E S R Y Y
## [926] N Z Q W N H J N I C O U R E B U D R N C E D D K Y X Z X F N K R O V J M F
## [963] F B I X S Y Q T A D U F D E V D V D H T K S H N M O Z A R A J D B J V R I
## [1000] U M W C N K D B Z T Z T W U F H U Z Z M D U C G B E F G O M R M M V Y T V
## [1037] W Q G C D F V L P T F G G V L Y L X R Y Q X E G F R S K B R L V N I M P D
## [1074] D O V X T J F I L E R A R V K L F N X Q A N X R Y M Z J B N S Y Z C C W R
## [1111] F P D R Q B S D R I X G W A J V L I D A I U I D G G V S R E M Y I E Q S S
## [1148] W S T Q I A A W A Y D Z B X E J K X G Q Q D U F A W Z O X T O H V E K O W
## [1185] D P F M Y J Q M R T Y Z J K F W N W G S D D H P M C R R H S W W M A N W Y
## [1222] D Z X C A N J D P E B U K P D H C P K U V N D M W D N S I L Q O F M S H U
## [1259] L M L I U I X A M J M A M I J C Y Y O E R L N P Q F I Z Q N Z W D Y B D I
## [1296] M E F C I U K S C X F A W I H Z F S W R I X P D Z B B Y A F M C N P O O B
## [1333] R S S H Q L Z Q Q Y F M U F H F V X I G E E X I H U V E O Q Z N Z I B U T
## [1370] C I Z I C Y V R Q C M S Y S E U Z R W Z W F K C Y N R B R Z K F N L P G Z
## [1407] Q X Y F S P Z M Z K I H I D O J D T V B J F O G Y C Y Y K I E T C H K Z U
## [1444] A K Q M F H Y S V K Y J W Q N S E P K S P Z S P U Z X D Q R A A G D E V Y
## [1481] G R F O D R E V O X M L E O Z X F D J V N P L N M J Z V X O M Z G I C E D
## [1518] B R U D S A K L F C T Q M M C O R U O O C K E C H A R E S E B G Z H V K H
## [1555] R A B S X C E B W R Z C A K F E V V Z S X K I P L L F R H B Q Y B H I N N
## [1592] U U F J L P W W P I T Z F I V S N C U K O X V X R Z L J N C E X R M E W Y
## [1629] F P B V L V L X N Y X J E C L G F D K R T Q F T X U B P E D H Z U K K R P
## [1666] M Q G X O B I U I Z Z K U B Q B Z L D T C B L M V H K Z N R T F S A Y A D
## [1703] R W L R L T V X D M D D H R U K U C J D N U F C Q Q B Q K I M O F B Q T D
## [1740] F W Q X M E P W B B D O J U M B P V H B A T Q S Y V S K O K H N P T J Q I
## [1777] K E I S J T D E A D D Q D I S X I Q O F R P J Q J L Z D K N Q G A P T N Q
## [1814] Q P I C M P N L I I P N B G L Q K Z V W C X C P B D G G H J P D G Z T W T
## [1851] I K H W T O B X G W F E Q X S V R Z F E I M H N H F F Z C G J G G B M E R
## [1888] I Q W X F A S W W I W S E A E J B I U Q J Y V P S N J G D W A D E Z W O E
## [1925] R Q F V G V K U K D I C P R O B K O V A H L E B I S G E S U X I T X D L W
## [1962] U D T E Y N Q H S G G S H C A Q I A A N H K Y A V L B U K V G H F X I F L
## [1999] Y Z Y M V Y X V J O M V Q N L X T D L Z U D K C X M C J H S K T T K U N K
## [2036] D B R O N V C W M G T I U U H D L D X U D P B M F G X I S J J D U H E Z T
## [2073] Q H R C S Z G U K V C A Q S J R R C I Z K F P R L P F X R U K D V B X P R
## [2110] U W R A S B E X N B J T F E I D U T F K A E M K J Y N K O M G Z I U J I T
## [2147] R K G F Q R G B L L U I D P Z J V N G D O U G R G X M U E M A G B F I R K
## [2184] W Z C N P P D Y J M A S T H R I U K G F J S J U E V E L B O X X J N K U A
## [2221] Z A O P A G M V J A F J U G D J I E R L L Y X M L T S N E M S Y Q M D K W
## [2258] P H C S Q I X I J A N I E U P O N P W O F I R D W M P T F A Y F P G U S V
## [2295] P K A H R R X J S N Y A D A E F G F V P S F T C K G H W H V L I B J V V B
## [2332] P T J P S G D F T T P B N S B S C R O R N G W I H Z G U Z G U B I U T H C
## [2369] E Y M E A J F X H A J K U Y Z V I F S B J C T M D N N N U P L W L M N O W
## [2406] L F U Y J Q K L C R H J L W F T I Y U K G K B R T F N U U A L V G R M F V
## [2443] I J Y W Z Z R C X R B J M K E Q N G F K J C W Y Q C M D A J M F B M E D E
## [2480] K I T S M T Q H E R Z W V F T R G L Y A N M P V V M I F A Z H O Q Z X G O
## [2517] N P L Z F P G P A Y T O E X T G I C O F Y A E E C O J X T D X Z P P Y A P
## [2554] W Y G C E W R U V M F Q M V X K A K G L B W V X A Z H B G V E D V V D P P
## [2591] H F A U Z W Z Z B F B E C K I Z J P P C S R Z N B G E A U E X K X T N V U
## [2628] U J B V N I G V Z B S C H C X F X H I S E W R L Y X K O X A A N P E K G G
## [2665] K B V D A T I B O Z Q K N X U T X D P N K M Z H G K W U E T Z R S L X C M
## [2702] Z Z Y F X C C F S I X O Q F S E W Y O W Z N V N P K F R D Z L V J L R G H
## [2739] U D W D I L Z V D F D Z F L Q X A G V I M G V A A O I M N W X L U I I Q U
## [2776] G L A I U B I O C G D C M S R V B O U I M H C W A J S H S T P T F O N O E
## [2813] V D T P J T I R H T K C I I I F D W E U V Y L I E M G R B H Y B G M P X G
## [2850] U K K I B D S Q L R Z K L S H D H K C S Z H Z B Q D F D I U E B I Z C H C
## [2887] Z I X W Z Y A N C B R U Q B M R X N H W E D I X E L B K R O C D V Z Q Q L
## [2924] O N U D A L Z B E N W B Q V G X I R E M W K C D I J T J L F Z Z V E X D K
## [2961] B K F C X S W G P D G U Z U M A V K A W R I D M E G O C J O J A S P V V T
## [2998] W G D O T B L C X Y P B K Y Q P T R I X B A P K K A Y Q E W J K U H F E M
## [3035] C C W H R D T E H D D G M G X G Y N P D L G A Q N A C S A I Y O L K D H K
## [3072] H Q S N X H A C K Q O S E I L K D C N Y T T M I J G I H E R X X Q K C K J
## [3109] U Q Z A N V S T X T S Q G L J P F F L D O B E D J T Y V W K C B Z W T F A
## [3146] W J N R J Y X B W V M M B G D W B U A A Y H M N Q Z C R P V C Q L B E Y K
## [3183] B D K R A M E G U M D R I O H R T E H W U A X T V T T J E N B R H F P P P
## [3220] N Z F B U D D P M O D B N T D E G Y Q E H X M C C I W W L T Z E P I S D H
## [3257] H U N O D M J E Q N T R R C L G K N G O D R B W F D L R W B A W C Y A M Z
## [3294] Y H V S B F T H C D T A I A H H T F Q U O M V R S R D R W K A Y P U Z P U
## [3331] D H R G F G K R U C O V M W R V M Y V C F T Z W E X X S F X B T C T J M Y
## [3368] E Q X Z U W N F Q O N P C K Q O T Z G Q S Q D D U U B D S Z R R U Q G Y N
## [3405] Z N R B I E X F N T U I P O G Z A X X A M R L G U U R M J C C H D N K G L
## [3442] I A H E V Y H V J U C V J H P V S M A J T I R I S N F C F U J T M X J O J
## [3479] Z N P S H H Y X P Z P F Z C H M N R X W P P S F Z M K C V J K U T W W Q P
## [3516] M G U G Z X F M J D X U H Q J Z B N F Z S P H O H Q L X F D L P Z A D T T
## [3553] U U Z F M Y Q O X X O U U X B D Q Y G E N V U K V L F Z I H X X G Y E D X
## [3590] N P F F R D A X A L Y F W B Y K I S I J X K G Y Y N D G T G R A O X C A C
## [3627] G A F D L F U P C Q J R P V G E T N Z U Y S Y H B Z J Z X N T B T Z Z Z R
## [3664] B J R J F M C N T L H M N H V A C W P B Y O T I E W K Q E H Q G Y Q R O R
## [3701] W X N A X I K Z T E N B C K D I E T I D M X H V R V R X H D F A P E O L O
## [3738] T J Q D C E N K A L I T Y E U E J B F N F T D P Q S E S T S X J F H G H S
## [3775] T B M N C Y Y F R N G S J N P C U K V S E F J Y Z D I Y B O R G A C Z I Y
## [3812] Q H M Y S O L F A N R D O N U U B H H J J I B P K E U U W Q E D M J T L S
## [3849] N P D X M A T L W P Q T L E W C C M G E T P W J E C R T U M T X K Y K M Z
## [3886] V G L A E X G A A H I L X M Z D V V L H C E Y D U F Q J G U X B T R Y G D
## [3923] B N M G L X L V P K W Q Q Q S D W O Y L T G C Z Q N T M O A T N F Q U H O
## [3960] S J A B F E F A Q C S F C W T N E L A K M R F Y V S M O L D F W D E J T D
## [3997] C T S A
## Levels: A B C D E F G H I J K L M N O P Q R S T U V W X Y Ztable(letter_predictions,letters_test$letter)##
## letter_predictions A B C D E F G H I J K L M N O
## A 144 0 0 0 0 0 0 0 0 1 0 0 1 2 2
## B 0 121 0 5 2 0 1 2 0 0 1 0 1 0 0
## C 0 0 120 0 4 0 10 2 2 0 1 3 0 0 2
## D 2 2 0 156 0 1 3 10 4 3 4 3 0 5 5
## E 0 0 5 0 127 3 1 1 0 0 3 4 0 0 0
## F 0 0 0 0 0 138 2 2 6 0 0 0 0 0 0
## G 1 1 2 1 9 2 123 2 0 0 1 2 1 0 1
## H 0 0 0 1 0 1 0 102 0 2 3 2 3 4 20
## I 0 1 0 0 0 1 0 0 141 8 0 0 0 0 0
## J 0 1 0 0 0 1 0 2 5 128 0 0 0 0 1
## K 1 1 9 0 0 0 2 5 0 0 118 0 0 2 0
## L 0 0 0 0 2 0 1 1 0 0 0 133 0 0 0
## M 0 0 1 1 0 0 1 1 0 0 0 0 135 4 0
## N 0 0 0 0 0 1 0 1 0 0 0 0 0 145 0
## O 1 0 2 1 0 0 1 2 0 1 0 0 0 1 99
## P 0 0 0 1 0 2 1 0 0 0 0 0 0 0 2
## Q 0 0 0 0 0 0 8 2 0 0 0 3 0 0 3
## R 0 7 0 0 1 0 3 8 0 0 13 0 0 1 1
## S 1 1 0 0 1 0 3 0 1 1 0 1 0 0 0
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##
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## O 3 3 0 0 0 3 0 0 0 0 0
## P 130 0 0 0 0 0 0 0 0 1 0
## Q 1 124 0 5 0 0 0 0 0 2 0
## R 1 0 138 0 1 0 1 0 0 0 0
## S 0 14 0 101 3 0 0 0 2 0 10
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## U 0 0 0 0 0 152 0 0 1 1 0
## V 0 3 1 0 0 0 126 1 0 4 0
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## X 0 0 0 1 0 0 0 0 137 1 1
## Y 7 0 0 0 3 0 0 0 0 127 0
## Z 0 0 0 18 3 0 0 0 0 0 132agreement<-letter_predictions==letters_test$letter
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## [829] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE FALSE FALSE
## [841] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE
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## [3997] TRUE TRUE TRUE TRUEletter_classifier_rbf<-ksvm(letter~.,data=letters_train,kernel = "rbfdot")
letter_predictions_rbf<-predict(letter_classifier_rbf,letters_test)
library(readxl)## Warning: package 'readxl' was built under R version 4.3.3ageandheight<- read_excel("C:/Users/Woodard2606/Downloads/AgeHeight.xlsx", sheet="Hoja")
ageandheight## # A tibble: 12 × 3
## age height no_siblings
## <dbl> <dbl> <dbl>
## 1 18 76.1 1
## 2 19 77 2
## 3 20 78.1 3
## 4 21 78.2 2
## 5 22 78.8 0
## 6 23 79.7 1
## 7 24 79.9 5
## 8 25 81.1 0
## 9 26 81.2 1
## 10 27 81.8 4
## 11 28 82.8 1
## 12 29 83.5 5ImHeight <- lm(height~age,data = ageandheight)
summary(ImHeight)##
## Call:
## lm(formula = height ~ age, data = ageandheight)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.27238 -0.24248 -0.02762 0.16014 0.47238
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 64.9283 0.5084 127.71 < 2e-16 ***
## age 0.6350 0.0214 29.66 4.43e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.256 on 10 degrees of freedom
## Multiple R-squared: 0.9888, Adjusted R-squared: 0.9876
## F-statistic: 880 on 1 and 10 DF, p-value: 4.428e-11ImHeight2 <- lm(height~age+no_siblings,data = ageandheight)
summary(ImHeight2)##
## Call:
## lm(formula = height ~ age + no_siblings, data = ageandheight)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.26297 -0.22462 -0.02021 0.16102 0.49752
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 64.90554 0.53526 121.260 8.96e-16 ***
## age 0.63751 0.02340 27.249 5.85e-10 ***
## no_siblings -0.01772 0.04735 -0.374 0.717
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2677 on 9 degrees of freedom
## Multiple R-squared: 0.9889, Adjusted R-squared: 0.9865
## F-statistic: 402.2 on 2 and 9 DF, p-value: 1.576e-09library(readr)
insurance <- read_csv("C:/Users/Woodard2606/Downloads/insurance.csv")## Rows: 1338 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): sex, smoker, region
## dbl (4): age, bmi, children, charges
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.View(insurance)
View(insurance)
str(insurance)## spc_tbl_ [1,338 × 7] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ age : num [1:1338] 19 18 28 33 32 31 46 37 37 60 ...
## $ sex : chr [1:1338] "female" "male" "male" "male" ...
## $ bmi : num [1:1338] 27.9 33.8 33 22.7 28.9 ...
## $ children: num [1:1338] 0 1 3 0 0 0 1 3 2 0 ...
## $ smoker : chr [1:1338] "yes" "no" "no" "no" ...
## $ region : chr [1:1338] "southwest" "southeast" "southeast" "northwest" ...
## $ charges : num [1:1338] 16885 1726 4449 21984 3867 ...
## - attr(*, "spec")=
## .. cols(
## .. age = col_double(),
## .. sex = col_character(),
## .. bmi = col_double(),
## .. children = col_double(),
## .. smoker = col_character(),
## .. region = col_character(),
## .. charges = col_double()
## .. )
## - attr(*, "problems")=<externalptr>summary(insurance)## age sex bmi children
## Min. :18.00 Length:1338 Min. :15.96 Min. :0.000
## 1st Qu.:27.00 Class :character 1st Qu.:26.30 1st Qu.:0.000
## Median :39.00 Mode :character Median :30.40 Median :1.000
## Mean :39.21 Mean :30.66 Mean :1.095
## 3rd Qu.:51.00 3rd Qu.:34.69 3rd Qu.:2.000
## Max. :64.00 Max. :53.13 Max. :5.000
## smoker region charges
## Length:1338 Length:1338 Min. : 1122
## Class :character Class :character 1st Qu.: 4740
## Mode :character Mode :character Median : 9382
## Mean :13270
## 3rd Qu.:16640
## Max. :63770hist(insurance$charges)
table(insurance$region)##
## northeast northwest southeast southwest
## 324 325 364 325table(insurance$smoker)##
## no yes
## 1064 274table(insurance$sex)##
## female male
## 662 676cor(insurance[c('age','bmi','children','charges')])## age bmi children charges
## age 1.0000000 0.1092719 0.04246900 0.29900819
## bmi 0.1092719 1.0000000 0.01275890 0.19834097
## children 0.0424690 0.0127589 1.00000000 0.06799823
## charges 0.2990082 0.1983410 0.06799823 1.00000000library(psych)## Warning: package 'psych' was built under R version 4.3.3##
## Attaching package: 'psych'
##
## The following object is masked from 'package:kernlab':
##
## alpha
##
## The following objects are masked from 'package:ggplot2':
##
## %+%, alphapairs(insurance[c('age','bmi','children','charges')])
pairs.panels(insurance[c('age','bmi','children','charges')])
ins_model<-lm(charges~age+children+bmi+sex+smoker+region,data=insurance)
summary(ins_model)##
## Call:
## lm(formula = charges ~ age + children + bmi + sex + smoker +
## region, data = insurance)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11304.9 -2848.1 -982.1 1393.9 29992.8
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -11938.5 987.8 -12.086 < 2e-16 ***
## age 256.9 11.9 21.587 < 2e-16 ***
## children 475.5 137.8 3.451 0.000577 ***
## bmi 339.2 28.6 11.860 < 2e-16 ***
## sexmale -131.3 332.9 -0.394 0.693348
## smokeryes 23848.5 413.1 57.723 < 2e-16 ***
## regionnorthwest -353.0 476.3 -0.741 0.458769
## regionsoutheast -1035.0 478.7 -2.162 0.030782 *
## regionsouthwest -960.0 477.9 -2.009 0.044765 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6062 on 1329 degrees of freedom
## Multiple R-squared: 0.7509, Adjusted R-squared: 0.7494
## F-statistic: 500.8 on 8 and 1329 DF, p-value: < 2.2e-16insurance$age2<-insurance$age^2
insurance$bmi30<-ifelse(insurance$bmi>30,1,0)
ins_model<-lm(charges~age+children+bmi+sex+smoker+bmi30+region+age2,data=insurance)
summary(ins_model)##
## Call:
## lm(formula = charges ~ age + children + bmi + sex + smoker +
## bmi30 + region + age2, data = insurance)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12525.0 -3381.3 160.9 1311.3 29258.3
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2852.423 1825.191 -1.563 0.118336
## age -26.238 80.416 -0.326 0.744261
## children 627.304 142.291 4.409 1.12e-05 ***
## bmi 148.123 46.045 3.217 0.001327 **
## sexmale -159.152 328.097 -0.485 0.627703
## smokeryes 23864.986 407.112 58.620 < 2e-16 ***
## bmi30 2813.900 547.483 5.140 3.16e-07 ***
## regionnorthwest -401.016 469.358 -0.854 0.393041
## regionsoutheast -884.246 472.549 -1.871 0.061534 .
## regionsouthwest -929.552 470.963 -1.974 0.048620 *
## age2 3.573 1.003 3.562 0.000382 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5973 on 1327 degrees of freedom
## Multiple R-squared: 0.7585, Adjusted R-squared: 0.7567
## F-statistic: 416.8 on 10 and 1327 DF, p-value: < 2.2e-16##charges~bmi30*smoker
ins_model2<-lm(charges~age+children+bmi+sex+smoker+bmi30+region+age2,data=insurance)
Ins_train<-insurance[1:1070,]
Ins_test<-insurance[-1070,]
Ins_train## # A tibble: 1,070 × 9
## age sex bmi children smoker region charges age2 bmi30
## <dbl> <chr> <dbl> <dbl> <chr> <chr> <dbl> <dbl> <dbl>
## 1 19 female 27.9 0 yes southwest 16885. 361 0
## 2 18 male 33.8 1 no southeast 1726. 324 1
## 3 28 male 33 3 no southeast 4449. 784 1
## 4 33 male 22.7 0 no northwest 21984. 1089 0
## 5 32 male 28.9 0 no northwest 3867. 1024 0
## 6 31 female 25.7 0 no southeast 3757. 961 0
## 7 46 female 33.4 1 no southeast 8241. 2116 1
## 8 37 female 27.7 3 no northwest 7282. 1369 0
## 9 37 male 29.8 2 no northeast 6406. 1369 0
## 10 60 female 25.8 0 no northwest 28923. 3600 0
## # ℹ 1,060 more rowsIncluding Plots
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