library(mlbench)## Warning: package 'mlbench' was built under R version 4.4.3data("BreastCancer")str(BreastCancer)## 'data.frame': 699 obs. of 11 variables:
## $ Id : chr "1000025" "1002945" "1015425" "1016277" ...
## $ Cl.thickness : Ord.factor w/ 10 levels "1"<"2"<"3"<"4"<..: 5 5 3 6 4 8 1 2 2 4 ...
## $ Cell.size : Ord.factor w/ 10 levels "1"<"2"<"3"<"4"<..: 1 4 1 8 1 10 1 1 1 2 ...
## $ Cell.shape : Ord.factor w/ 10 levels "1"<"2"<"3"<"4"<..: 1 4 1 8 1 10 1 2 1 1 ...
## $ Marg.adhesion : Ord.factor w/ 10 levels "1"<"2"<"3"<"4"<..: 1 5 1 1 3 8 1 1 1 1 ...
## $ Epith.c.size : Ord.factor w/ 10 levels "1"<"2"<"3"<"4"<..: 2 7 2 3 2 7 2 2 2 2 ...
## $ Bare.nuclei : Factor w/ 10 levels "1","2","3","4",..: 1 10 2 4 1 10 10 1 1 1 ...
## $ Bl.cromatin : Factor w/ 10 levels "1","2","3","4",..: 3 3 3 3 3 9 3 3 1 2 ...
## $ Normal.nucleoli: Factor w/ 10 levels "1","2","3","4",..: 1 2 1 7 1 7 1 1 1 1 ...
## $ Mitoses : Factor w/ 9 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 5 1 ...
## $ Class : Factor w/ 2 levels "benign","malignant": 1 1 1 1 1 2 1 1 1 1 ...levels(BreastCancer$Class)## [1] "benign" "malignant"summary(BreastCancer)## Id Cl.thickness Cell.size Cell.shape Marg.adhesion
## Length:699 1 :145 1 :384 1 :353 1 :407
## Class :character 5 :130 10 : 67 2 : 59 2 : 58
## Mode :character 3 :108 3 : 52 10 : 58 3 : 58
## 4 : 80 2 : 45 3 : 56 10 : 55
## 10 : 69 4 : 40 4 : 44 4 : 33
## 2 : 50 5 : 30 5 : 34 8 : 25
## (Other):117 (Other): 81 (Other): 95 (Other): 63
## Epith.c.size Bare.nuclei Bl.cromatin Normal.nucleoli Mitoses
## 2 :386 1 :402 2 :166 1 :443 1 :579
## 3 : 72 10 :132 3 :165 10 : 61 2 : 35
## 4 : 48 2 : 30 1 :152 3 : 44 3 : 33
## 1 : 47 5 : 30 7 : 73 2 : 36 10 : 14
## 6 : 41 3 : 28 4 : 40 8 : 24 4 : 12
## 5 : 39 (Other): 61 5 : 34 6 : 22 7 : 9
## (Other): 66 NA's : 16 (Other): 69 (Other): 69 (Other): 17
## Class
## benign :458
## malignant:241
##
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## library(mice) #library untuk mengatasi nilai yang hilang## Warning: package 'mice' was built under R version 4.4.3##
## Attaching package: 'mice'## The following object is masked from 'package:stats':
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## filter## The following objects are masked from 'package:base':
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## cbind, rbindlibrary(caret) #library untuk training dan ploting model## Warning: package 'caret' was built under R version 4.4.3## Loading required package: ggplot2## Loading required package: latticedataset_impute <- mice(BreastCancer[,2:10], print = FALSE) #Menghapus nilai yang hilang dan ID dari dataset
BreastCancer <- cbind(BreastCancer[,11, drop = FALSE], mice::complete(dataset_impute, 1)) #Menambahkan kelas target ke dataset yang diperhitungkan tanpa nilai yang hilangsummary(BreastCancer)## Class Cl.thickness Cell.size Cell.shape Marg.adhesion
## benign :458 1 :145 1 :384 1 :353 1 :407
## malignant:241 5 :130 10 : 67 2 : 59 2 : 58
## 3 :108 3 : 52 10 : 58 3 : 58
## 4 : 80 2 : 45 3 : 56 10 : 55
## 10 : 69 4 : 40 4 : 44 4 : 33
## 2 : 50 5 : 30 5 : 34 8 : 25
## (Other):117 (Other): 81 (Other): 95 (Other): 63
## Epith.c.size Bare.nuclei Bl.cromatin Normal.nucleoli Mitoses
## 2 :386 1 :413 2 :166 1 :443 1 :579
## 3 : 72 10 :133 3 :165 10 : 61 2 : 35
## 4 : 48 2 : 31 1 :152 3 : 44 3 : 33
## 1 : 47 5 : 30 7 : 73 2 : 36 10 : 14
## 6 : 41 3 : 28 4 : 40 8 : 24 4 : 12
## 5 : 39 8 : 21 5 : 34 6 : 22 7 : 9
## (Other): 66 (Other): 43 (Other): 69 (Other): 69 (Other): 17library(caTools) #pembagian dataset## Warning: package 'caTools' was built under R version 4.4.3set.seed(150)
split=sample.split(BreastCancer, SplitRatio = 0.7) #Membagi dataset menjadi data training dan data testing
training_set=subset(BreastCancer,split==TRUE) #Dataset Training
test_set=subset(BreastCancer,split==FALSE) #Dtaset Testingdim(training_set) #Dimensi data training## [1] 490 10dim(test_set)## [1] 209 10topredict_set<-test_set[2:10] #Menghapus Target Class
dim(topredict_set)## [1] 209 9library(e1071)## Warning: package 'e1071' was built under R version 4.4.3model_naive <- naiveBayes(Class ~ ., data = training_set) #Implementasi Naive Bayes
preds_naive <- predict(model_naive, newdata = topredict_set) #Memprediksi Target Class untuk validasi
(conf_matrix_naive <- table(preds_naive, test_set$Class))##
## preds_naive benign malignant
## benign 129 2
## malignant 6 72confusionMatrix(conf_matrix_naive)## Confusion Matrix and Statistics
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##
## preds_naive benign malignant
## benign 129 2
## malignant 6 72
##
## Accuracy : 0.9617
## 95% CI : (0.926, 0.9833)
## No Information Rate : 0.6459
## P-Value [Acc > NIR] : <2e-16
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## Kappa : 0.9173
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## Mcnemar's Test P-Value : 0.2888
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## Sensitivity : 0.9556
## Specificity : 0.9730
## Pos Pred Value : 0.9847
## Neg Pred Value : 0.9231
## Prevalence : 0.6459
## Detection Rate : 0.6172
## Detection Prevalence : 0.6268
## Balanced Accuracy : 0.9643
##
## 'Positive' Class : benign
## #Random Forest
library(randomForest)## Warning: package 'randomForest' was built under R version 4.4.3## 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':
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## marginmodel_rf <- randomForest(Class ~ ., data = training_set, importance=TRUE, ntree = 5) #Implementasi Random Forest
preds_rf <- predict(model_rf, topredict_set)
(conf_matrix_forest <- table(preds_rf, test_set$Class))##
## preds_rf benign malignant
## benign 128 2
## malignant 7 72confusionMatrix(conf_matrix_forest)## Confusion Matrix and Statistics
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##
## preds_rf benign malignant
## benign 128 2
## malignant 7 72
##
## Accuracy : 0.9569
## 95% CI : (0.9198, 0.9801)
## No Information Rate : 0.6459
## P-Value [Acc > NIR] : <2e-16
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## Kappa : 0.9073
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## Mcnemar's Test P-Value : 0.1824
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## Sensitivity : 0.9481
## Specificity : 0.9730
## Pos Pred Value : 0.9846
## Neg Pred Value : 0.9114
## Prevalence : 0.6459
## Detection Rate : 0.6124
## Detection Prevalence : 0.6220
## Balanced Accuracy : 0.9606
##
## 'Positive' Class : benign
## #DecisionTree Classifier
library(rpart)## Warning: package 'rpart' was built under R version 4.4.3model_dtree<- rpart(Class ~ ., data = training_set) #Implementasi Decision Tree
preds_dtree <- predict(model_dtree,newdata=topredict_set, type = "class")(conf_matrix_dtree <- table(preds_dtree, test_set$Class))##
## preds_dtree benign malignant
## benign 127 5
## malignant 8 69confusionMatrix(conf_matrix_dtree)## Confusion Matrix and Statistics
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##
## preds_dtree benign malignant
## benign 127 5
## malignant 8 69
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## Accuracy : 0.9378
## 95% CI : (0.896, 0.9665)
## No Information Rate : 0.6459
## P-Value [Acc > NIR] : <2e-16
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## Kappa : 0.8652
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## Mcnemar's Test P-Value : 0.5791
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## Sensitivity : 0.9407
## Specificity : 0.9324
## Pos Pred Value : 0.9621
## Neg Pred Value : 0.8961
## Prevalence : 0.6459
## Detection Rate : 0.6077
## Detection Prevalence : 0.6316
## Balanced Accuracy : 0.9366
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## 'Positive' Class : benign
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