breast_cancer
ozlemtuna
2024-08-01
data <- read.csv("~/data.csv")
head(data)## id diagnosis radius_mean texture_mean perimeter_mean area_mean
## 1 842302 M 17.99 10.38 122.80 1001.0
## 2 842517 M 20.57 17.77 132.90 1326.0
## 3 84300903 M 19.69 21.25 130.00 1203.0
## 4 84348301 M 11.42 20.38 77.58 386.1
## 5 84358402 M 20.29 14.34 135.10 1297.0
## 6 843786 M 12.45 15.70 82.57 477.1
## smoothness_mean compactness_mean concavity_mean concave.points_mean
## 1 0.11840 0.27760 0.3001 0.14710
## 2 0.08474 0.07864 0.0869 0.07017
## 3 0.10960 0.15990 0.1974 0.12790
## 4 0.14250 0.28390 0.2414 0.10520
## 5 0.10030 0.13280 0.1980 0.10430
## 6 0.12780 0.17000 0.1578 0.08089
## symmetry_mean fractal_dimension_mean radius_se texture_se perimeter_se
## 1 0.2419 0.07871 1.0950 0.9053 8.589
## 2 0.1812 0.05667 0.5435 0.7339 3.398
## 3 0.2069 0.05999 0.7456 0.7869 4.585
## 4 0.2597 0.09744 0.4956 1.1560 3.445
## 5 0.1809 0.05883 0.7572 0.7813 5.438
## 6 0.2087 0.07613 0.3345 0.8902 2.217
## area_se smoothness_se compactness_se concavity_se concave.points_se
## 1 153.40 0.006399 0.04904 0.05373 0.01587
## 2 74.08 0.005225 0.01308 0.01860 0.01340
## 3 94.03 0.006150 0.04006 0.03832 0.02058
## 4 27.23 0.009110 0.07458 0.05661 0.01867
## 5 94.44 0.011490 0.02461 0.05688 0.01885
## 6 27.19 0.007510 0.03345 0.03672 0.01137
## symmetry_se fractal_dimension_se radius_worst texture_worst perimeter_worst
## 1 0.03003 0.006193 25.38 17.33 184.60
## 2 0.01389 0.003532 24.99 23.41 158.80
## 3 0.02250 0.004571 23.57 25.53 152.50
## 4 0.05963 0.009208 14.91 26.50 98.87
## 5 0.01756 0.005115 22.54 16.67 152.20
## 6 0.02165 0.005082 15.47 23.75 103.40
## area_worst smoothness_worst compactness_worst concavity_worst
## 1 2019.0 0.1622 0.6656 0.7119
## 2 1956.0 0.1238 0.1866 0.2416
## 3 1709.0 0.1444 0.4245 0.4504
## 4 567.7 0.2098 0.8663 0.6869
## 5 1575.0 0.1374 0.2050 0.4000
## 6 741.6 0.1791 0.5249 0.5355
## concave.points_worst symmetry_worst fractal_dimension_worst X
## 1 0.2654 0.4601 0.11890 NA
## 2 0.1860 0.2750 0.08902 NA
## 3 0.2430 0.3613 0.08758 NA
## 4 0.2575 0.6638 0.17300 NA
## 5 0.1625 0.2364 0.07678 NA
## 6 0.1741 0.3985 0.12440 NAsummary(data)## id diagnosis radius_mean texture_mean
## Min. : 8670 Length:569 Min. : 6.981 Min. : 9.71
## 1st Qu.: 869218 Class :character 1st Qu.:11.700 1st Qu.:16.17
## Median : 906024 Mode :character Median :13.370 Median :18.84
## Mean : 30371831 Mean :14.127 Mean :19.29
## 3rd Qu.: 8813129 3rd Qu.:15.780 3rd Qu.:21.80
## Max. :911320502 Max. :28.110 Max. :39.28
## perimeter_mean area_mean smoothness_mean compactness_mean
## Min. : 43.79 Min. : 143.5 Min. :0.05263 Min. :0.01938
## 1st Qu.: 75.17 1st Qu.: 420.3 1st Qu.:0.08637 1st Qu.:0.06492
## Median : 86.24 Median : 551.1 Median :0.09587 Median :0.09263
## Mean : 91.97 Mean : 654.9 Mean :0.09636 Mean :0.10434
## 3rd Qu.:104.10 3rd Qu.: 782.7 3rd Qu.:0.10530 3rd Qu.:0.13040
## Max. :188.50 Max. :2501.0 Max. :0.16340 Max. :0.34540
## concavity_mean concave.points_mean symmetry_mean fractal_dimension_mean
## Min. :0.00000 Min. :0.00000 Min. :0.1060 Min. :0.04996
## 1st Qu.:0.02956 1st Qu.:0.02031 1st Qu.:0.1619 1st Qu.:0.05770
## Median :0.06154 Median :0.03350 Median :0.1792 Median :0.06154
## Mean :0.08880 Mean :0.04892 Mean :0.1812 Mean :0.06280
## 3rd Qu.:0.13070 3rd Qu.:0.07400 3rd Qu.:0.1957 3rd Qu.:0.06612
## Max. :0.42680 Max. :0.20120 Max. :0.3040 Max. :0.09744
## radius_se texture_se perimeter_se area_se
## Min. :0.1115 Min. :0.3602 Min. : 0.757 Min. : 6.802
## 1st Qu.:0.2324 1st Qu.:0.8339 1st Qu.: 1.606 1st Qu.: 17.850
## Median :0.3242 Median :1.1080 Median : 2.287 Median : 24.530
## Mean :0.4052 Mean :1.2169 Mean : 2.866 Mean : 40.337
## 3rd Qu.:0.4789 3rd Qu.:1.4740 3rd Qu.: 3.357 3rd Qu.: 45.190
## Max. :2.8730 Max. :4.8850 Max. :21.980 Max. :542.200
## smoothness_se compactness_se concavity_se concave.points_se
## Min. :0.001713 Min. :0.002252 Min. :0.00000 Min. :0.000000
## 1st Qu.:0.005169 1st Qu.:0.013080 1st Qu.:0.01509 1st Qu.:0.007638
## Median :0.006380 Median :0.020450 Median :0.02589 Median :0.010930
## Mean :0.007041 Mean :0.025478 Mean :0.03189 Mean :0.011796
## 3rd Qu.:0.008146 3rd Qu.:0.032450 3rd Qu.:0.04205 3rd Qu.:0.014710
## Max. :0.031130 Max. :0.135400 Max. :0.39600 Max. :0.052790
## symmetry_se fractal_dimension_se radius_worst texture_worst
## Min. :0.007882 Min. :0.0008948 Min. : 7.93 Min. :12.02
## 1st Qu.:0.015160 1st Qu.:0.0022480 1st Qu.:13.01 1st Qu.:21.08
## Median :0.018730 Median :0.0031870 Median :14.97 Median :25.41
## Mean :0.020542 Mean :0.0037949 Mean :16.27 Mean :25.68
## 3rd Qu.:0.023480 3rd Qu.:0.0045580 3rd Qu.:18.79 3rd Qu.:29.72
## Max. :0.078950 Max. :0.0298400 Max. :36.04 Max. :49.54
## perimeter_worst area_worst smoothness_worst compactness_worst
## Min. : 50.41 Min. : 185.2 Min. :0.07117 Min. :0.02729
## 1st Qu.: 84.11 1st Qu.: 515.3 1st Qu.:0.11660 1st Qu.:0.14720
## Median : 97.66 Median : 686.5 Median :0.13130 Median :0.21190
## Mean :107.26 Mean : 880.6 Mean :0.13237 Mean :0.25427
## 3rd Qu.:125.40 3rd Qu.:1084.0 3rd Qu.:0.14600 3rd Qu.:0.33910
## Max. :251.20 Max. :4254.0 Max. :0.22260 Max. :1.05800
## concavity_worst concave.points_worst symmetry_worst fractal_dimension_worst
## Min. :0.0000 Min. :0.00000 Min. :0.1565 Min. :0.05504
## 1st Qu.:0.1145 1st Qu.:0.06493 1st Qu.:0.2504 1st Qu.:0.07146
## Median :0.2267 Median :0.09993 Median :0.2822 Median :0.08004
## Mean :0.2722 Mean :0.11461 Mean :0.2901 Mean :0.08395
## 3rd Qu.:0.3829 3rd Qu.:0.16140 3rd Qu.:0.3179 3rd Qu.:0.09208
## Max. :1.2520 Max. :0.29100 Max. :0.6638 Max. :0.20750
## X
## Mode:logical
## NA's:569
##
##
##
## data <- data[,-1]Boxplotlar
par(mfrow=c(2,5))
for(i in 2:31) {
boxplot(data[,i], main=names(data)[i])
}
par(mfrow=c(2,5))
for(i in 2:31) {
plot(density(data[,i]), main=names(data)[i])
}
#par(mfrow=c(1,1))
library(corrplot)## corrplot 0.92 loaded# Korelasyonun hesaplanması
correlations <- cor(data[,3:32]) # Korelasyon grafiği oluşturma
corrplot(correlations, method="circle")
library(caret)## Loading required package: ggplot2## Loading required package: latticelibrary(randomForest)## randomForest 4.7-1.1## Type rfNews() to see new features/changes/bug fixes.##
## Attaching package: 'randomForest'## The following object is masked from 'package:ggplot2':
##
## margincontrol <- trainControl(method="repeatedcv", number=10, repeats=3)
data$diagnosis <- as.factor(data$diagnosis)# Check for missing values in each column
missing_summary <- sapply(data, function(x) sum(is.na(x)))
# Remove rows with any missing values
data_clean <- na.omit(data)
data_clean <- data[, !names(data) %in% c("X")]Doğruluğu geliştirme
set.seed(7)
split <- createDataPartition(y=data_clean$diagnosis, p=0.7, list=FALSE)
trainData <- data_clean[split,]
testData <- data_clean[-split,]
set.seed(7)
metric <- "Accuracy"
tunegrid <- expand.grid(.mtry=c(2:5))
rf_gridsearch <- train(diagnosis~., data=trainData, method="rf", metric=metric, tuneGrid=tunegrid, trControl=control)
bestmtry = rf_gridsearch$bestTune
print(rf_gridsearch)## Random Forest
##
## 399 samples
## 30 predictor
## 2 classes: 'B', 'M'
##
## No pre-processing
## Resampling: Cross-Validated (10 fold, repeated 3 times)
## Summary of sample sizes: 360, 359, 359, 359, 359, 359, ...
## Resampling results across tuning parameters:
##
## mtry Accuracy Kappa
## 2 0.9524145 0.8972375
## 3 0.9499145 0.8921589
## 4 0.9524145 0.8976500
## 5 0.9507479 0.8944420
##
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was mtry = 4.plot(rf_gridsearch)
print(bestmtry$mtry)## [1] 4# Load necessary libraries
library(caret)
library(randomForest)
# Initialize an empty list to store models
modellist <- list()
# Set seed for reproducibility
set.seed(7)
# Loop over different numbers of trees and mtry values
for (ntree in c(500, 1500, 2500, 3500, 4500)) {
for (mtry in c(2, 4, 6, 8, 10)) {
# Define the tuning grid for mtry
tunegrid <- expand.grid(.mtry = mtry)
# Train the model with the specified parameters
fit <- train(diagnosis ~ .,
data = trainData,
method = "rf",
metric = metric,
tuneGrid = tunegrid,
trControl = control,
ntree = ntree)
# Use a unique key combining ntree and mtry to store the model
key <- paste("ntree", ntree, "mtry", mtry, sep = "_")
modellist[[key]] <- fit
}
}
# Compare results using resamples
results <- resamples(modellist)
# Summary of results
summary(results)##
## Call:
## summary.resamples(object = results)
##
## Models: ntree_500_mtry_2, ntree_500_mtry_4, ntree_500_mtry_6, ntree_500_mtry_8, ntree_500_mtry_10, ntree_1500_mtry_2, ntree_1500_mtry_4, ntree_1500_mtry_6, ntree_1500_mtry_8, ntree_1500_mtry_10, ntree_2500_mtry_2, ntree_2500_mtry_4, ntree_2500_mtry_6, ntree_2500_mtry_8, ntree_2500_mtry_10, ntree_3500_mtry_2, ntree_3500_mtry_4, ntree_3500_mtry_6, ntree_3500_mtry_8, ntree_3500_mtry_10, ntree_4500_mtry_2, ntree_4500_mtry_4, ntree_4500_mtry_6, ntree_4500_mtry_8, ntree_4500_mtry_10
## Number of resamples: 30
##
## Accuracy
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## ntree_500_mtry_2 0.8750000 0.9250000 0.9500000 0.9532479 0.9937500 1.000 0
## ntree_500_mtry_4 0.8500000 0.9309295 0.9500000 0.9489957 0.9750000 1.000 0
## ntree_500_mtry_6 0.8750000 0.9250000 0.9487179 0.9465171 0.9750000 1.000 0
## ntree_500_mtry_8 0.8750000 0.9250000 0.9500000 0.9482479 0.9750000 1.000 0
## ntree_500_mtry_10 0.9000000 0.9250000 0.9500000 0.9498932 0.9750000 1.000 0
## ntree_1500_mtry_2 0.8500000 0.9309295 0.9743590 0.9549145 0.9750000 1.000 0
## ntree_1500_mtry_4 0.9000000 0.9250000 0.9500000 0.9473932 0.9748397 0.975 0
## ntree_1500_mtry_6 0.8500000 0.9490385 0.9500000 0.9524145 0.9750000 1.000 0
## ntree_1500_mtry_8 0.8500000 0.9312500 0.9500000 0.9507265 0.9750000 1.000 0
## ntree_1500_mtry_10 0.9000000 0.9250000 0.9500000 0.9523291 0.9750000 1.000 0
## ntree_2500_mtry_2 0.8750000 0.9312500 0.9500000 0.9523932 0.9750000 1.000 0
## ntree_2500_mtry_4 0.8500000 0.9250000 0.9500000 0.9498718 0.9750000 1.000 0
## ntree_2500_mtry_6 0.8974359 0.9250000 0.9500000 0.9514744 0.9750000 1.000 0
## ntree_2500_mtry_8 0.9000000 0.9250000 0.9500000 0.9490598 0.9750000 1.000 0
## ntree_2500_mtry_10 0.8250000 0.9312500 0.9500000 0.9506624 0.9750000 1.000 0
## ntree_3500_mtry_2 0.8750000 0.9309295 0.9500000 0.9557692 0.9750000 1.000 0
## ntree_3500_mtry_4 0.8750000 0.9250000 0.9500000 0.9499359 0.9750000 1.000 0
## ntree_3500_mtry_6 0.8500000 0.9250000 0.9500000 0.9482479 0.9750000 1.000 0
## ntree_3500_mtry_8 0.8500000 0.9250000 0.9500000 0.9491239 0.9750000 1.000 0
## ntree_3500_mtry_10 0.8750000 0.9250000 0.9500000 0.9507906 0.9750000 1.000 0
## ntree_4500_mtry_2 0.8750000 0.9250000 0.9500000 0.9516239 0.9750000 1.000 0
## ntree_4500_mtry_4 0.8750000 0.9250000 0.9500000 0.9499145 0.9750000 1.000 0
## ntree_4500_mtry_6 0.8974359 0.9250000 0.9500000 0.9507051 0.9750000 1.000 0
## ntree_4500_mtry_8 0.8750000 0.9250000 0.9500000 0.9506624 0.9750000 1.000 0
## ntree_4500_mtry_10 0.8750000 0.9250000 0.9500000 0.9507479 0.9750000 1.000 0
##
## Kappa
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## ntree_500_mtry_2 0.7297297 0.8378378 0.8918721 0.8989958 0.9868421 1.0000000
## ntree_500_mtry_4 0.6800000 0.8496173 0.8933333 0.8904725 0.9459459 1.0000000
## ntree_500_mtry_6 0.7222222 0.8378378 0.8867636 0.8845775 0.9473684 1.0000000
## ntree_500_mtry_8 0.7222222 0.8389047 0.8933333 0.8888189 0.9459459 1.0000000
## ntree_500_mtry_10 0.7746479 0.8378378 0.8947186 0.8922721 0.9470128 1.0000000
## ntree_1500_mtry_2 0.6619718 0.8537218 0.9433962 0.9024974 0.9459459 1.0000000
## ntree_1500_mtry_4 0.7808219 0.8421053 0.8918721 0.8870054 0.9457464 0.9473684
## ntree_1500_mtry_6 0.6883117 0.8890313 0.8933333 0.8979058 0.9459459 1.0000000
## ntree_1500_mtry_8 0.6800000 0.8541817 0.8933333 0.8943531 0.9459459 1.0000000
## ntree_1500_mtry_10 0.7808219 0.8421053 0.8933333 0.8974950 0.9459459 1.0000000
## ntree_2500_mtry_2 0.7297297 0.8541817 0.8933333 0.8978135 0.9459459 1.0000000
## ntree_2500_mtry_4 0.6800000 0.8378378 0.8933333 0.8925057 0.9470128 1.0000000
## ntree_2500_mtry_6 0.7771429 0.8389047 0.8933333 0.8960357 0.9459459 1.0000000
## ntree_2500_mtry_8 0.7808219 0.8421053 0.8911889 0.8905659 0.9459459 1.0000000
## ntree_2500_mtry_10 0.6315789 0.8541817 0.8933333 0.8944633 0.9459459 1.0000000
## ntree_3500_mtry_2 0.7222222 0.8537218 0.8933333 0.9046287 0.9459459 1.0000000
## ntree_3500_mtry_4 0.7297297 0.8389047 0.8933333 0.8924848 0.9459459 1.0000000
## ntree_3500_mtry_6 0.6619718 0.8378378 0.8933333 0.8883906 0.9459459 1.0000000
## ntree_3500_mtry_8 0.6800000 0.8431174 0.8933333 0.8909390 0.9459459 1.0000000
## ntree_3500_mtry_10 0.7435897 0.8389047 0.8933333 0.8949027 0.9459459 1.0000000
## ntree_4500_mtry_2 0.7297297 0.8389047 0.8918721 0.8953407 0.9473684 1.0000000
## ntree_4500_mtry_4 0.7368421 0.8421053 0.8918721 0.8923278 0.9459459 1.0000000
## ntree_4500_mtry_6 0.7839335 0.8421053 0.8904110 0.8944626 0.9459459 1.0000000
## ntree_4500_mtry_8 0.7368421 0.8431174 0.8933333 0.8944207 0.9459459 1.0000000
## ntree_4500_mtry_10 0.7297297 0.8389047 0.8933333 0.8943792 0.9459459 1.0000000
## NA's
## ntree_500_mtry_2 0
## ntree_500_mtry_4 0
## ntree_500_mtry_6 0
## ntree_500_mtry_8 0
## ntree_500_mtry_10 0
## ntree_1500_mtry_2 0
## ntree_1500_mtry_4 0
## ntree_1500_mtry_6 0
## ntree_1500_mtry_8 0
## ntree_1500_mtry_10 0
## ntree_2500_mtry_2 0
## ntree_2500_mtry_4 0
## ntree_2500_mtry_6 0
## ntree_2500_mtry_8 0
## ntree_2500_mtry_10 0
## ntree_3500_mtry_2 0
## ntree_3500_mtry_4 0
## ntree_3500_mtry_6 0
## ntree_3500_mtry_8 0
## ntree_3500_mtry_10 0
## ntree_4500_mtry_2 0
## ntree_4500_mtry_4 0
## ntree_4500_mtry_6 0
## ntree_4500_mtry_8 0
## ntree_4500_mtry_10 0# Load necessary libraries
library(ggplot2)
# Define the number of trees and mtry values
n_trees <- c(500, 500, 500, 500, 500,
1500, 1500, 1500, 1500, 1500,
2500, 2500, 2500, 2500, 2500,
3500, 3500, 3500, 3500, 3500,
4500, 4500, 4500, 4500, 4500)
mtry_values <- rep(c(2, 4, 6, 8, 10), 5)
# Mean accuracy values for each combination of ntree and mtry
mean_accuracies <- c(0.9532479, 0.9489957, 0.9465171, 0.9482479, 0.9498932,
0.9549145, 0.9473932, 0.9524145, 0.9507265, 0.9523291,
0.9523932, 0.9498718, 0.9514744, 0.9490598, 0.9506624,
0.9557692, 0.9499359, 0.9482479, 0.9491239, 0.9507906,
0.9516239, 0.9499145, 0.9507051, 0.9506624, 0.9507479)
# Create a data frame for plotting
plot_data <- data.frame(ntrees = n_trees, mtry = mtry_values, accuracy = mean_accuracies)
# Plot with ggplot2
ggplot(plot_data, aes(x = ntrees, y = accuracy, color = as.factor(mtry))) +
geom_line() + # Line plot
geom_point() + # Points on the line
labs(title = "Mean Accuracy vs Number of Trees for Different mtry Values",
x = "Number of Trees (ntree)",
y = "Mean Accuracy",
color = "mtry") +
theme_minimal() # Minimal theme for a clean look
dotplot(results)
# Load necessary libraries
library(ggplot2)
# Define the data
ntree_values <- c(rep(500, 5), rep(1500, 5), rep(2500, 5), rep(3500, 5), rep(4500, 5))
mtry_values <- rep(c(2, 4, 6, 8, 10), 5)
mean_accuracies <- c(0.9532479, 0.9489957, 0.9465171, 0.9482479, 0.9498932,
0.9549145, 0.9473932, 0.9524145, 0.9507265, 0.9523291,
0.9523932, 0.9498718, 0.9514744, 0.9490598, 0.9506624,
0.9557692, 0.9499359, 0.9482479, 0.9491239, 0.9507906,
0.9516239, 0.9499145, 0.9507051, 0.9506624, 0.9507479)
# Create a data frame for plotting
plot_data <- data.frame(ntree = factor(ntree_values), mtry = factor(mtry_values), accuracy = mean_accuracies)
# Plot with ggplot2
ggplot(plot_data, aes(x = mtry, y = ntree, fill = accuracy)) +
geom_tile(color = "white") + # Create tiles with white borders
scale_fill_gradient(low = "white", high = "blue") + # Gradient color scale
labs(title = "Heatmap of Mean Accuracy by Number of Trees and mtry",
x = "mtry",
y = "Number of Trees (ntree)",
fill = "Mean Accuracy") +
theme_minimal() + # Minimal theme for a clean look
theme(axis.text.x = element_text(angle = 45, hjust = 1)) # Angle x-axis labels for readability
chosenNTree <- 3500
chosenmtry <- bestmtry$mtry
rf_ <- randomForest(trainData, y=trainData$diagnosis, data=trainData, proximity=TRUE, ntree=chosenNTree,nodesize=2,importance=TRUE, mtry=chosenmtry)
print(rf_)##
## Call:
## randomForest(x = trainData, y = trainData$diagnosis, ntree = chosenNTree, mtry = chosenmtry, nodesize = 2, importance = TRUE, proximity = TRUE, data = trainData)
## Type of random forest: classification
## Number of trees: 3500
## No. of variables tried at each split: 4
##
## OOB estimate of error rate: 0.75%
## Confusion matrix:
## B M class.error
## B 249 1 0.00400000
## M 2 147 0.01342282importance(rf_, type=1)## MeanDecreaseAccuracy
## diagnosis 71.101031
## radius_mean 20.525906
## texture_mean 16.774487
## perimeter_mean 20.229724
## area_mean 23.093348
## smoothness_mean 15.802729
## compactness_mean 13.051200
## concavity_mean 23.417504
## concave.points_mean 27.304953
## symmetry_mean 5.875787
## fractal_dimension_mean 8.093922
## radius_se 17.726080
## texture_se 3.521332
## perimeter_se 16.701481
## area_se 24.018449
## smoothness_se 8.205297
## compactness_se 9.484645
## concavity_se 11.949030
## concave.points_se 10.574872
## symmetry_se 3.947371
## fractal_dimension_se 8.821390
## radius_worst 30.070275
## texture_worst 19.144128
## perimeter_worst 31.573565
## area_worst 29.758342
## smoothness_worst 21.514254
## compactness_worst 15.616311
## concavity_worst 23.992580
## concave.points_worst 30.897505
## symmetry_worst 14.453829
## fractal_dimension_worst 11.645477predictions <- predict(rf_, newdata = testData)
confusionMatrix(predictions, testData$diagnosis)## Confusion Matrix and Statistics
##
## Reference
## Prediction B M
## B 107 0
## M 0 63
##
## Accuracy : 1
## 95% CI : (0.9785, 1)
## No Information Rate : 0.6294
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 1
##
## Mcnemar's Test P-Value : NA
##
## Sensitivity : 1.0000
## Specificity : 1.0000
## Pos Pred Value : 1.0000
## Neg Pred Value : 1.0000
## Prevalence : 0.6294
## Detection Rate : 0.6294
## Detection Prevalence : 0.6294
## Balanced Accuracy : 1.0000
##
## 'Positive' Class : B
## table(predictions, testData$diagnosis)##
## predictions B M
## B 107 0
## M 0 63