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第100+15步 ChatGPT学习:R实现Ababoost分类

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Ababoost分类

(1)导入数据

我习惯用RStudio自带的导入功能:

(2)建立Ababoost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$X)# Define control method for training with cross-validation
trainControl <- trainControl(method = "cv", number = 10)# Fit Random Forest model on the training set
model <- train(X ~ ., data = trainData, method = "ada", trControl = trainControl)# Print the best parameters found by the model
best_params <- model$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]# 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,使用 caret 包训练Ababoost模型时,最关键的可调参数不多,下面是一些可以调整的关键参数:

①Iter: 这是最重要的参数之一,代表弱学习器的数量,即AdaBoost算法中的迭代次数。较大的nIter值通常可以提高模型的复杂度和拟合能力,但也可能导致过拟合。

②maxdepth: 这是决策树的最大深度。AdaBoost通常使用决策树作为其弱学习器。通过调整maxdepth可以控制单个决策树的复杂度,从而影响整个集成模型的复杂度。

③nu: 这个参数是学习率(也称为收缩参数或步长)。它用于更新每次迭代中模型权重。较小的nu值可以使模型学习得更加谨慎,通常可以减少过拟合的风险,但可能需要更多的迭代次数来收敛。

结果输出(默认参数):

在默认参数中,caret包已经默默帮我们吧上面三个参数进行测试和寻优。

从AUC来看,Ababoost随便一跑,就跑出个不错的结果。不过有些过拟合了,验证集的性能稍微差些。

三、Ababoost手动调参方法(3个值)

设置iter值取值50、100、200、400、600;maxdepth取值1、2、5、7和9;nu取值0.01、0.1、0.5:

# 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$X)# Define control method for training with cross-validation
trainControl <- trainControl(method = "cv", number = 10)# Define the tuning grid with correct parameter names
tuneGrid <- expand.grid(iter = c(50, 100, 200, 400, 600),maxdepth = c(1, 2, 5, 7, 9),nu = c(0.01, 0.1, 0.5))# Train the model using the ada method and the corrected tuning grid
model <- train(X ~ ., data = trainData, method = "ada", trControl = trainControl, tuneGrid = tuneGrid)# Print the best parameters found by the model
best_params <- model$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]# 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:

还不让入默认的性能好呢。

看看GPT给的参数的取值建议,祝各位调得开心:

iter (迭代次数): 这个参数通常设置在10到1000之间。较小的数据集可能需要较少的迭代,而较大或较复杂的数据集可能需要更多的迭代。通常开始可以尝试50, 100, 200等值,然后根据模型的性能来调整。

maxdepth (树的最大深度): 这个参数一般设置在1到10之间。深度为1意味着使用决策树桩(仅一个决策点),这有助于防止过拟合,是AdaBoost中常用的设置。但对于更复杂的数据模式,可能需要更深的树。可以尝试的值包括1, 2, 3, 5等。

nu (学习率): 学习率的典型取值范围是0.01到1。较小的学习率(如0.01, 0.1)可以使模型学习得更稳健,但收敛速度可能较慢,需要更多的迭代次数。较高的学习率可以加快学习速度,但可能导致模型在训练过程中不稳定。

四、最后

数据嘛:

链接:https://pan.baidu.com/s/1rEf6JZyzA1ia5exoq5OF7g?pwd=x8xm

提取码:x8xm

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