百度关键词网站怎么做,做的网站没有手机版,wordpress链接title属性,深圳网站制作培训基于R 4.2.2版本演示 一、写在前面
有不少大佬问做机器学习分类能不能用R语言#xff0c;不想学Python咯。
答曰#xff1a;可#xff01;用GPT或者Kimi转一下就得了呗。
加上最近也没啥内容写了#xff0c;就帮各位搬运一下吧。 二、R代码实现决策树分类
#xff08;…基于R 4.2.2版本演示 一、写在前面
有不少大佬问做机器学习分类能不能用R语言不想学Python咯。
答曰可用GPT或者Kimi转一下就得了呗。
加上最近也没啥内容写了就帮各位搬运一下吧。 二、R代码实现决策树分类
1导入数据
我习惯用RStudio自带的导入功能 2建立决策树模型默认参数
# Load necessary libraries
library(caret)
library(pROC)
library(ggplot2)# Assume data is your dataframe containing the data
# Set seed to ensure reproducibility
set.seed(123)# Split data into training and validation sets (80% training, 20% validation)
trainIndex - createDataPartition(data$X, p 0.8, list FALSE)
trainData - data[trainIndex, ]
validData - data[-trainIndex, ]# Convert the target variable to a factor for classification
trainData$X - as.factor(trainData$X)
validData$X - as.factor(validData$X)# Define control method for training with cross-validation
trainControl - trainControl(method cv, number 10)
# Fit Decision Tree model on the training set
model - train(X ~ ., data trainData, method rpart, trControl trainControl)# Print the best parameters found by the model
best_params - model$bestTune
cat(The best parameters found are:\n)
print(best_params)# Predict on the training and validation sets
trainPredict - predict(model, trainData, type prob)[,2]
validPredict - predict(model, validData, type prob)[,2]# Convert true values to factor for ROC analysis
trainData$X - as.factor(trainData$X)
validData$X - as.factor(validData$X)# Calculate ROC curves and AUC values
trainRoc - roc(response trainData$X, predictor trainPredict)
validRoc - roc(response validData$X, predictor validPredict)# Plot ROC curves with AUC values
ggplot(data data.frame(fpr trainRoc$specificities, tpr trainRoc$sensitivities), aes(x 1 - fpr, y tpr)) geom_line(color blue) geom_area(alpha 0.2, fill blue) geom_abline(slope 1, intercept 0, linetype dashed, color black) ggtitle(Training ROC Curve) xlab(False Positive Rate) ylab(True Positive Rate) annotate(text, x 0.5, y 0.1, label paste(Training AUC , round(auc(trainRoc), 2)), hjust 0.5, color blue)ggplot(data data.frame(fpr validRoc$specificities, tpr validRoc$sensitivities), aes(x 1 - fpr, y tpr)) geom_line(color red) geom_area(alpha 0.2, fill red) geom_abline(slope 1, intercept 0, linetype dashed, color black) ggtitle(Validation ROC Curve) xlab(False Positive Rate) ylab(True Positive Rate) annotate(text, x 0.5, y 0.2, label paste(Validation AUC , round(auc(validRoc), 2)), hjust 0.5, color red)# Calculate confusion matrices based on 0.5 cutoff for probability
confMatTrain - table(trainData$X, trainPredict 0.5)
confMatValid - table(validData$X, validPredict 0.5)# Function to plot confusion matrix using ggplot2
plot_confusion_matrix - function(conf_mat, dataset_name) {conf_mat_df - as.data.frame(as.table(conf_mat))colnames(conf_mat_df) - c(Actual, Predicted, Freq)p - ggplot(data conf_mat_df, aes(x Predicted, y Actual, fill Freq)) geom_tile(color white) geom_text(aes(label Freq), vjust 1.5, color black, size 5) scale_fill_gradient(low white, high steelblue) labs(title paste(Confusion Matrix -, dataset_name, Set), x Predicted Class, y Actual Class) theme_minimal() theme(axis.text.x element_text(angle 45, hjust 1), plot.title element_text(hjust 0.5))print(p)
}
# Now call the function to plot and display the confusion matrices
plot_confusion_matrix(confMatTrain, Training)
plot_confusion_matrix(confMatValid, Validation)# Extract values for calculations
a_train - confMatTrain[1, 1]
b_train - confMatTrain[1, 2]
c_train - confMatTrain[2, 1]
d_train - confMatTrain[2, 2]a_valid - confMatValid[1, 1]
b_valid - confMatValid[1, 2]
c_valid - confMatValid[2, 1]
d_valid - confMatValid[2, 2]# Training Set Metrics
acc_train - (a_train d_train) / sum(confMatTrain)
error_rate_train - 1 - acc_train
sen_train - d_train / (d_train c_train)
sep_train - a_train / (a_train b_train)
precision_train - d_train / (b_train d_train)
F1_train - (2 * precision_train * sen_train) / (precision_train sen_train)
MCC_train - (d_train * a_train - b_train * c_train) / sqrt((d_train b_train) * (d_train c_train) * (a_train b_train) * (a_train c_train))
auc_train - roc(response trainData$X, predictor trainPredict)$auc# Validation Set Metrics
acc_valid - (a_valid d_valid) / sum(confMatValid)
error_rate_valid - 1 - acc_valid
sen_valid - d_valid / (d_valid c_valid)
sep_valid - a_valid / (a_valid b_valid)
precision_valid - d_valid / (b_valid d_valid)
F1_valid - (2 * precision_valid * sen_valid) / (precision_valid sen_valid)
MCC_valid - (d_valid * a_valid - b_valid * c_valid) / sqrt((d_valid b_valid) * (d_valid c_valid) * (a_valid b_valid) * (a_valid c_valid))
auc_valid - roc(response validData$X, predictor validPredict)$auc# Print Metrics
cat(Training Metrics\n)
cat(Accuracy:, acc_train, \n)
cat(Error Rate:, error_rate_train, \n)
cat(Sensitivity:, sen_train, \n)
cat(Specificity:, sep_train, \n)
cat(Precision:, precision_train, \n)
cat(F1 Score:, F1_train, \n)
cat(MCC:, MCC_train, \n)
cat(AUC:, auc_train, \n\n)cat(Validation Metrics\n)
cat(Accuracy:, acc_valid, \n)
cat(Error Rate:, error_rate_valid, \n)
cat(Sensitivity:, sen_valid, \n)
cat(Specificity:, sep_valid, \n)
cat(Precision:, precision_valid, \n)
cat(F1 Score:, F1_valid, \n)
cat(MCC:, MCC_valid, \n)
cat(AUC:, auc_valid, \n)
在R语言中还是使用caret包来训练决策树模型可以调整多种参数来优化模型的性能
①cp (Complexity Parameter): 用来控制树的生长。cp 值越大生成的模型越简单。如果 cp 设置得太高可能导致模型欠拟合。
②maxdepth: 决定了树的最大深度。较深的树可以更好地捕捉数据中的复杂关系但也可能导致过拟合。
③minsplit: 定义了节点在尝试分裂之前所需的最小样本数。增加这个值可以让树更加稳健但也可能导致欠拟合。
④minbucket: 叶节点最少包含的样本数。这个参数可以帮助防止模型过于复杂从而避免过拟合。
结果输出默认参数 在默认参数中caret包只会默默帮我们找几个合适的cp值进行测试。其他三个参数就一个默认值。 三、决策树调参方法仅cp值
如前所述决策树的关键参数就是有4个但是caret包做网格搜索的话只能提供对于cp值的遍历其余三个不提供。
比如我设置cp值从0.001到0.1步长是0.001而maxdepth 20, minsplit 10, minbucket 10
# Load necessary libraries
library(caret)
library(pROC)
library(ggplot2)
library(rpart)# Assume data is your dataframe containing the data
# Set seed to ensure reproducibility
set.seed(123)# Split data into training and validation sets (80% training, 20% validation)
trainIndex - createDataPartition(data$X, p 0.8, list FALSE)
trainData - data[trainIndex, ]
validData - data[-trainIndex, ]# Convert the target variable to a factor for classification
trainData$X - as.factor(trainData$X)
validData$X - as.factor(validData$X)# 定义交叉验证控制方法
trainControl - trainControl(method cv, number 10)# 定义参数网格只包括 cp
tuneGrid - expand.grid(cp seq(0.001, 0.1, by 0.001))# 定义 rpart.control固定其他参数
rpartControl - rpart.control(maxdepth 20, minsplit 10, minbucket 10)# 使用 rpart 方法训练决策树模型
model - train(X ~ ., data trainData, method rpart, trControl trainControl, tuneGrid tuneGrid,control rpartControl)# 打印找到的最佳参数
best_params - model$bestTune
cat(The best parameters found are:\n)
print(best_params)# Predict on the training and validation sets
trainPredict - predict(model, trainData, type prob)[,2]
validPredict - predict(model, validData, type prob)[,2]# Convert true values to factor for ROC analysis
trainData$X - as.factor(trainData$X)
validData$X - as.factor(validData$X)# Calculate ROC curves and AUC values
trainRoc - roc(response trainData$X, predictor trainPredict)
validRoc - roc(response validData$X, predictor validPredict)# Plot ROC curves with AUC values
ggplot(data data.frame(fpr trainRoc$specificities, tpr trainRoc$sensitivities), aes(x 1 - fpr, y tpr)) geom_line(color blue) geom_area(alpha 0.2, fill blue) geom_abline(slope 1, intercept 0, linetype dashed, color black) ggtitle(Training ROC Curve) xlab(False Positive Rate) ylab(True Positive Rate) annotate(text, x 0.5, y 0.1, label paste(Training AUC , round(auc(trainRoc), 2)), hjust 0.5, color blue)ggplot(data data.frame(fpr validRoc$specificities, tpr validRoc$sensitivities), aes(x 1 - fpr, y tpr)) geom_line(color red) geom_area(alpha 0.2, fill red) geom_abline(slope 1, intercept 0, linetype dashed, color black) ggtitle(Validation ROC Curve) xlab(False Positive Rate) ylab(True Positive Rate) annotate(text, x 0.5, y 0.2, label paste(Validation AUC , round(auc(validRoc), 2)), hjust 0.5, color red)# Calculate confusion matrices based on 0.5 cutoff for probability
confMatTrain - table(trainData$X, trainPredict 0.5)
confMatValid - table(validData$X, validPredict 0.5)# Function to plot confusion matrix using ggplot2
plot_confusion_matrix - function(conf_mat, dataset_name) {conf_mat_df - as.data.frame(as.table(conf_mat))colnames(conf_mat_df) - c(Actual, Predicted, Freq)p - ggplot(data conf_mat_df, aes(x Predicted, y Actual, fill Freq)) geom_tile(color white) geom_text(aes(label Freq), vjust 1.5, color black, size 5) scale_fill_gradient(low white, high steelblue) labs(title paste(Confusion Matrix -, dataset_name, Set), x Predicted Class, y Actual Class) theme_minimal() theme(axis.text.x element_text(angle 45, hjust 1), plot.title element_text(hjust 0.5))print(p)
}
# Now call the function to plot and display the confusion matrices
plot_confusion_matrix(confMatTrain, Training)
plot_confusion_matrix(confMatValid, Validation)# Extract values for calculations
a_train - confMatTrain[1, 1]
b_train - confMatTrain[1, 2]
c_train - confMatTrain[2, 1]
d_train - confMatTrain[2, 2]a_valid - confMatValid[1, 1]
b_valid - confMatValid[1, 2]
c_valid - confMatValid[2, 1]
d_valid - confMatValid[2, 2]# Training Set Metrics
acc_train - (a_train d_train) / sum(confMatTrain)
error_rate_train - 1 - acc_train
sen_train - d_train / (d_train c_train)
sep_train - a_train / (a_train b_train)
precision_train - d_train / (b_train d_train)
F1_train - (2 * precision_train * sen_train) / (precision_train sen_train)
MCC_train - (d_train * a_train - b_train * c_train) / sqrt((d_train b_train) * (d_train c_train) * (a_train b_train) * (a_train c_train))
auc_train - roc(response trainData$X, predictor trainPredict)$auc# Validation Set Metrics
acc_valid - (a_valid d_valid) / sum(confMatValid)
error_rate_valid - 1 - acc_valid
sen_valid - d_valid / (d_valid c_valid)
sep_valid - a_valid / (a_valid b_valid)
precision_valid - d_valid / (b_valid d_valid)
F1_valid - (2 * precision_valid * sen_valid) / (precision_valid sen_valid)
MCC_valid - (d_valid * a_valid - b_valid * c_valid) / sqrt((d_valid b_valid) * (d_valid c_valid) * (a_valid b_valid) * (a_valid c_valid))
auc_valid - roc(response validData$X, predictor validPredict)$auc# Print Metrics
cat(Training Metrics\n)
cat(Accuracy:, acc_train, \n)
cat(Error Rate:, error_rate_train, \n)
cat(Sensitivity:, sen_train, \n)
cat(Specificity:, sep_train, \n)
cat(Precision:, precision_train, \n)
cat(F1 Score:, F1_train, \n)
cat(MCC:, MCC_train, \n)
cat(AUC:, auc_train, \n\n)cat(Validation Metrics\n)
cat(Accuracy:, acc_valid, \n)
cat(Error Rate:, error_rate_valid, \n)
cat(Sensitivity:, sen_valid, \n)
cat(Specificity:, sep_valid, \n)
cat(Precision:, precision_valid, \n)
cat(F1 Score:, F1_valid, \n)
cat(MCC:, MCC_valid, \n)
cat(AUC:, auc_valid, \n)
结果输出 似乎好了一点了那么如果我先把其余三个参数也纳入遍历呢那只能用循环语句了。 四、决策树调参方法4个值
设置cp值从0.001到0.1步长是0.001maxdepth从10到30步长是5minsplit取值10、20、30、40minbucket取值5、10、15、20
# Load necessary libraries
library(caret)
library(pROC)
library(ggplot2)
library(rpart)# Assume data is your dataframe containing the data
# Set seed to ensure reproducibility
set.seed(123)# Split data into training and validation sets (80% training, 20% validation)
trainIndex - createDataPartition(data$X, p 0.8, list FALSE)
trainData - data[trainIndex, ]
validData - data[-trainIndex, ]# Convert the target variable to a factor for classification
trainData$X - as.factor(trainData$X)
validData$X - as.factor(validData$X)# 参数网格定义
cp_values - seq(0.001, 0.01, by 0.001)
maxdepth_values - seq(10, 30, by 5)
minsplit_values - c(10, 20, 30, 40)
minbucket_values - c(5, 10, 15, 20)# 用于存储结果的列表
results - list()# 网格搜索实现
for (cp in cp_values) {for (maxdepth in maxdepth_values) {for (minsplit in minsplit_values) {for (minbucket in minbucket_values) {# 训练模型model - rpart(X ~ ., data trainData, control rpart.control(cp cp, maxdepth maxdepth,minsplit minsplit, minbucket minbucket))# 预测验证集predictions - predict(model, validData, type class)# 计算性能指标这里使用准确度accuracy - sum(predictions validData$X) / nrow(validData)# 存储结果results[[length(results) 1]] - list(cp cp, maxdepth maxdepth, minsplit minsplit, minbucket minbucket,accuracy accuracy)}}}
}# 找到最高准确度的模型参数
best_model - results[[which.max(sapply(results, function(x) x$accuracy))]]
print(best_model)# Predict on the training and validation sets
trainPredict - predict(model, trainData, type prob)[,2]
validPredict - predict(model, validData, type prob)[,2]# Convert true values to factor for ROC analysis
trainData$X - as.factor(trainData$X)
validData$X - as.factor(validData$X)# Calculate ROC curves and AUC values
trainRoc - roc(response trainData$X, predictor trainPredict)
validRoc - roc(response validData$X, predictor validPredict)# Plot ROC curves with AUC values
ggplot(data data.frame(fpr trainRoc$specificities, tpr trainRoc$sensitivities), aes(x 1 - fpr, y tpr)) geom_line(color blue) geom_area(alpha 0.2, fill blue) geom_abline(slope 1, intercept 0, linetype dashed, color black) ggtitle(Training ROC Curve) xlab(False Positive Rate) ylab(True Positive Rate) annotate(text, x 0.5, y 0.1, label paste(Training AUC , round(auc(trainRoc), 2)), hjust 0.5, color blue)ggplot(data data.frame(fpr validRoc$specificities, tpr validRoc$sensitivities), aes(x 1 - fpr, y tpr)) geom_line(color red) geom_area(alpha 0.2, fill red) geom_abline(slope 1, intercept 0, linetype dashed, color black) ggtitle(Validation ROC Curve) xlab(False Positive Rate) ylab(True Positive Rate) annotate(text, x 0.5, y 0.2, label paste(Validation AUC , round(auc(validRoc), 2)), hjust 0.5, color red)# Calculate confusion matrices based on 0.5 cutoff for probability
confMatTrain - table(trainData$X, trainPredict 0.5)
confMatValid - table(validData$X, validPredict 0.5)# Function to plot confusion matrix using ggplot2
plot_confusion_matrix - function(conf_mat, dataset_name) {conf_mat_df - as.data.frame(as.table(conf_mat))colnames(conf_mat_df) - c(Actual, Predicted, Freq)p - ggplot(data conf_mat_df, aes(x Predicted, y Actual, fill Freq)) geom_tile(color white) geom_text(aes(label Freq), vjust 1.5, color black, size 5) scale_fill_gradient(low white, high steelblue) labs(title paste(Confusion Matrix -, dataset_name, Set), x Predicted Class, y Actual Class) theme_minimal() theme(axis.text.x element_text(angle 45, hjust 1), plot.title element_text(hjust 0.5))print(p)
}
# Now call the function to plot and display the confusion matrices
plot_confusion_matrix(confMatTrain, Training)
plot_confusion_matrix(confMatValid, Validation)# Extract values for calculations
a_train - confMatTrain[1, 1]
b_train - confMatTrain[1, 2]
c_train - confMatTrain[2, 1]
d_train - confMatTrain[2, 2]a_valid - confMatValid[1, 1]
b_valid - confMatValid[1, 2]
c_valid - confMatValid[2, 1]
d_valid - confMatValid[2, 2]# Training Set Metrics
acc_train - (a_train d_train) / sum(confMatTrain)
error_rate_train - 1 - acc_train
sen_train - d_train / (d_train c_train)
sep_train - a_train / (a_train b_train)
precision_train - d_train / (b_train d_train)
F1_train - (2 * precision_train * sen_train) / (precision_train sen_train)
MCC_train - (d_train * a_train - b_train * c_train) / sqrt((d_train b_train) * (d_train c_train) * (a_train b_train) * (a_train c_train))
auc_train - roc(response trainData$X, predictor trainPredict)$auc# Validation Set Metrics
acc_valid - (a_valid d_valid) / sum(confMatValid)
error_rate_valid - 1 - acc_valid
sen_valid - d_valid / (d_valid c_valid)
sep_valid - a_valid / (a_valid b_valid)
precision_valid - d_valid / (b_valid d_valid)
F1_valid - (2 * precision_valid * sen_valid) / (precision_valid sen_valid)
MCC_valid - (d_valid * a_valid - b_valid * c_valid) / sqrt((d_valid b_valid) * (d_valid c_valid) * (a_valid b_valid) * (a_valid c_valid))
auc_valid - roc(response validData$X, predictor validPredict)$auc# Print Metrics
cat(Training Metrics\n)
cat(Accuracy:, acc_train, \n)
cat(Error Rate:, error_rate_train, \n)
cat(Sensitivity:, sen_train, \n)
cat(Specificity:, sep_train, \n)
cat(Precision:, precision_train, \n)
cat(F1 Score:, F1_train, \n)
cat(MCC:, MCC_train, \n)
cat(AUC:, auc_train, \n\n)cat(Validation Metrics\n)
cat(Accuracy:, acc_valid, \n)
cat(Error Rate:, error_rate_valid, \n)
cat(Sensitivity:, sen_valid, \n)
cat(Specificity:, sep_valid, \n)
cat(Precision:, precision_valid, \n)
cat(F1 Score:, F1_valid, \n)
cat(MCC:, MCC_valid, \n)
cat(AUC:, auc_valid, \n)
结果输出 以上是找到的相对最优参数组合看看具体性能 哈哈又给调回去了矫枉过正。思路就是这么个思路大家自行食用了。 五、最后
看到这里我觉得还是Python的sk-learn提供的调参简单些至少不用写循环。
数据嘛
链接https://pan.baidu.com/s/1rEf6JZyzA1ia5exoq5OF7g?pwdx8xm
提取码x8xm
