YanQiWeek5CodingPractice1
Yan Qi
2025-09-25
STEP 1.Follow Along
5.5.1 Decision trees
library(mlbench)## Warning: package 'mlbench' was built under R version 4.5.1data(Sonar)
head(Sonar)## V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11
## 1 0.0200 0.0371 0.0428 0.0207 0.0954 0.0986 0.1539 0.1601 0.3109 0.2111 0.1609
## 2 0.0453 0.0523 0.0843 0.0689 0.1183 0.2583 0.2156 0.3481 0.3337 0.2872 0.4918
## 3 0.0262 0.0582 0.1099 0.1083 0.0974 0.2280 0.2431 0.3771 0.5598 0.6194 0.6333
## 4 0.0100 0.0171 0.0623 0.0205 0.0205 0.0368 0.1098 0.1276 0.0598 0.1264 0.0881
## 5 0.0762 0.0666 0.0481 0.0394 0.0590 0.0649 0.1209 0.2467 0.3564 0.4459 0.4152
## 6 0.0286 0.0453 0.0277 0.0174 0.0384 0.0990 0.1201 0.1833 0.2105 0.3039 0.2988
## V12 V13 V14 V15 V16 V17 V18 V19 V20 V21 V22
## 1 0.1582 0.2238 0.0645 0.0660 0.2273 0.3100 0.2999 0.5078 0.4797 0.5783 0.5071
## 2 0.6552 0.6919 0.7797 0.7464 0.9444 1.0000 0.8874 0.8024 0.7818 0.5212 0.4052
## 3 0.7060 0.5544 0.5320 0.6479 0.6931 0.6759 0.7551 0.8929 0.8619 0.7974 0.6737
## 4 0.1992 0.0184 0.2261 0.1729 0.2131 0.0693 0.2281 0.4060 0.3973 0.2741 0.3690
## 5 0.3952 0.4256 0.4135 0.4528 0.5326 0.7306 0.6193 0.2032 0.4636 0.4148 0.4292
## 6 0.4250 0.6343 0.8198 1.0000 0.9988 0.9508 0.9025 0.7234 0.5122 0.2074 0.3985
## V23 V24 V25 V26 V27 V28 V29 V30 V31 V32 V33
## 1 0.4328 0.5550 0.6711 0.6415 0.7104 0.8080 0.6791 0.3857 0.1307 0.2604 0.5121
## 2 0.3957 0.3914 0.3250 0.3200 0.3271 0.2767 0.4423 0.2028 0.3788 0.2947 0.1984
## 3 0.4293 0.3648 0.5331 0.2413 0.5070 0.8533 0.6036 0.8514 0.8512 0.5045 0.1862
## 4 0.5556 0.4846 0.3140 0.5334 0.5256 0.2520 0.2090 0.3559 0.6260 0.7340 0.6120
## 5 0.5730 0.5399 0.3161 0.2285 0.6995 1.0000 0.7262 0.4724 0.5103 0.5459 0.2881
## 6 0.5890 0.2872 0.2043 0.5782 0.5389 0.3750 0.3411 0.5067 0.5580 0.4778 0.3299
## V34 V35 V36 V37 V38 V39 V40 V41 V42 V43 V44
## 1 0.7547 0.8537 0.8507 0.6692 0.6097 0.4943 0.2744 0.0510 0.2834 0.2825 0.4256
## 2 0.2341 0.1306 0.4182 0.3835 0.1057 0.1840 0.1970 0.1674 0.0583 0.1401 0.1628
## 3 0.2709 0.4232 0.3043 0.6116 0.6756 0.5375 0.4719 0.4647 0.2587 0.2129 0.2222
## 4 0.3497 0.3953 0.3012 0.5408 0.8814 0.9857 0.9167 0.6121 0.5006 0.3210 0.3202
## 5 0.0981 0.1951 0.4181 0.4604 0.3217 0.2828 0.2430 0.1979 0.2444 0.1847 0.0841
## 6 0.2198 0.1407 0.2856 0.3807 0.4158 0.4054 0.3296 0.2707 0.2650 0.0723 0.1238
## V45 V46 V47 V48 V49 V50 V51 V52 V53 V54 V55
## 1 0.2641 0.1386 0.1051 0.1343 0.0383 0.0324 0.0232 0.0027 0.0065 0.0159 0.0072
## 2 0.0621 0.0203 0.0530 0.0742 0.0409 0.0061 0.0125 0.0084 0.0089 0.0048 0.0094
## 3 0.2111 0.0176 0.1348 0.0744 0.0130 0.0106 0.0033 0.0232 0.0166 0.0095 0.0180
## 4 0.4295 0.3654 0.2655 0.1576 0.0681 0.0294 0.0241 0.0121 0.0036 0.0150 0.0085
## 5 0.0692 0.0528 0.0357 0.0085 0.0230 0.0046 0.0156 0.0031 0.0054 0.0105 0.0110
## 6 0.1192 0.1089 0.0623 0.0494 0.0264 0.0081 0.0104 0.0045 0.0014 0.0038 0.0013
## V56 V57 V58 V59 V60 Class
## 1 0.0167 0.0180 0.0084 0.0090 0.0032 R
## 2 0.0191 0.0140 0.0049 0.0052 0.0044 R
## 3 0.0244 0.0316 0.0164 0.0095 0.0078 R
## 4 0.0073 0.0050 0.0044 0.0040 0.0117 R
## 5 0.0015 0.0072 0.0048 0.0107 0.0094 R
## 6 0.0089 0.0057 0.0027 0.0051 0.0062 Rlibrary("rpart")
m <- rpart(Class ~ ., data = Sonar, method = "class")
library("rpart.plot")## Warning: package 'rpart.plot' was built under R version 4.5.1rpart.plot(m)
p <- predict(m, Sonar, type = "class")
table(p, Sonar$Class)##
## p M R
## M 95 10
## R 16 87Training a random forest
library(caret)## Loading required package: ggplot2## Loading required package: latticeset.seed(12)
model <- train(Class ~ .,
data = Sonar,
method = "ranger")
print(model)## Random Forest
##
## 208 samples
## 60 predictor
## 2 classes: 'M', 'R'
##
## No pre-processing
## Resampling: Bootstrapped (25 reps)
## Summary of sample sizes: 208, 208, 208, 208, 208, 208, ...
## Resampling results across tuning parameters:
##
## mtry splitrule Accuracy Kappa
## 2 gini 0.8090731 0.6131571
## 2 extratrees 0.8136902 0.6234492
## 31 gini 0.7736954 0.5423516
## 31 extratrees 0.8285153 0.6521921
## 60 gini 0.7597299 0.5140905
## 60 extratrees 0.8157646 0.6255929
##
## Tuning parameter 'min.node.size' was held constant at a value of 1
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were mtry = 31, splitrule = extratrees
## and min.node.size = 1.plot(model)
model <- train(Class ~ ., data = Sonar, method = "ranger", tuneLength = 5)set.seed(42)
myGrid <- expand.grid(mtry = c(5, 10, 20, 40, 60),
splitrule = c("gini", "extratrees"),
min.node.size = 1)
model <- train(Class ~ .,
data = Sonar,
method = "ranger",
tuneGrid = myGrid,
trControl = trainControl(method = "cv",
number = 5,
verboseIter = FALSE))
print(model)## Random Forest
##
## 208 samples
## 60 predictor
## 2 classes: 'M', 'R'
##
## No pre-processing
## Resampling: Cross-Validated (5 fold)
## Summary of sample sizes: 166, 167, 167, 167, 165
## Resampling results across tuning parameters:
##
## mtry splitrule Accuracy Kappa
## 5 gini 0.8076277 0.6098253
## 5 extratrees 0.8416579 0.6784745
## 10 gini 0.7927667 0.5799348
## 10 extratrees 0.8418848 0.6791453
## 20 gini 0.7882316 0.5718852
## 20 extratrees 0.8516355 0.6991879
## 40 gini 0.7880048 0.5716461
## 40 extratrees 0.8371229 0.6695638
## 60 gini 0.7833482 0.5613525
## 60 extratrees 0.8322448 0.6599318
##
## Tuning parameter 'min.node.size' was held constant at a value of 1
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were mtry = 20, splitrule = extratrees
## and min.node.size = 1.plot(model)
Challenge
set.seed(42)
model <- train(Class ~ .,
data = Sonar,
method = "ranger",
tuneLength = 5,
trControl = trainControl(method = "cv",
number = 5,
verboseIter = FALSE))
plot(model)
STEP 2.
_ Randomly split your data into a training set (80%) and a test set (20%)
# check the data
chocolate <- read.csv("chocolate_tibble.csv")
head(chocolate)## final_grade review_date cocoa_percent company_location bean_type
## 1 3.75 2016 0.63 France
## 2 2.75 2015 0.70 France
## 3 3.00 2015 0.70 France
## 4 3.50 2015 0.70 France
## 5 3.50 2015 0.70 France
## 6 2.75 2014 0.70 France Criollo
## broad_bean_origin
## 1 Sao Tome
## 2 Togo
## 3 Togo
## 4 Togo
## 5 Peru
## 6 Venezuelalibrary(caret)
set.seed(1234)
trainIndex <- createDataPartition(chocolate$final_grade, p = 0.8, list = FALSE)
chocolate_train <- chocolate[trainIndex,]
chocolate_test <- chocolate[-trainIndex, ]decision_tree
library(parsnip)## Warning: package 'parsnip' was built under R version 4.5.1spec <- decision_tree() %>%
set_mode("regression") %>%
set_engine("rpart")
print(spec)## Decision Tree Model Specification (regression)
##
## Computational engine: rpartmodel <- spec %>%
parsnip ::fit(formula = final_grade ~ ., data = chocolate_train)
library(rpart)
model2 <- rpart(final_grade ~ cocoa_percent + company_location, data=chocolate_train, method = "anova")
library(party)## Warning: package 'party' was built under R version 4.5.1## Loading required package: grid## Loading required package: mvtnorm## Loading required package: modeltools## Loading required package: stats4##
## Attaching package: 'modeltools'## The following object is masked from 'package:parsnip':
##
## fit## Loading required package: strucchange## Warning: package 'strucchange' was built under R version 4.5.1## Loading required package: zoo##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric## Loading required package: sandwich## Warning: package 'sandwich' was built under R version 4.5.1model3 <- ctree(
final_grade ~ cocoa_percent + as.factor(company_location),
data= chocolate_train
)library(rpart.plot)
rpart.plot(model2, box.palette = "RdBu", shadow.col ="grey", nn=TRUE)
plot(model3)
Hyperparameters
decision_tree(tree_depth = 1) %>%
set_mode("regression") %>%
set_engine("rpart") %>%
parsnip ::fit(formula = final_grade ~ ., data = chocolate_train)## parsnip model object
##
## n= 1438
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 1438 323.17940 3.187065
## 2) cocoa_percent>=0.885 28 10.66741 2.401786 *
## 3) cocoa_percent< 0.885 1410 294.90250 3.202660 *Random Forest
library(MASS)
data(package = "MASS")
boston <- Boston
dim(Boston)## [1] 506 14names(boston)## [1] "crim" "zn" "indus" "chas" "nox" "rm" "age"
## [8] "dis" "rad" "tax" "ptratio" "black" "lstat" "medv"# training sample with 300 observations
library(randomForest)## randomForest 4.7-1.2## Type rfNews() to see new features/changes/bug fixes.##
## Attaching package: 'randomForest'## The following object is masked from 'package:ggplot2':
##
## margintrain = sample(1:nrow(Boston), 300)
Boston.rf = randomForest(medv ~ ., data = Boston, subset=train)plot(Boston.rf)
# evaluating variable importance
importance(Boston.rf)## IncNodePurity
## crim 1835.3270
## zn 289.1031
## indus 1687.7424
## chas 188.9720
## nox 1297.0798
## rm 8042.0495
## age 628.4363
## dis 1019.2070
## rad 210.7702
## tax 875.1236
## ptratio 1734.5538
## black 471.1634
## lstat 6523.7743varImpPlot(Boston.rf)