第100+14步 ChatGPT学习：R实现随机森林分类

  • 开发
  • 17

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代码实现随机森林分类

（1）导入数据

我习惯用RStudio自带的导入功能：

（2）建立随机森林模型（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 = "rf", 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 包训练随机森林模型时，最常见的可调参数是 mtry，但还有其他几个参数可以根据需要调整。这些参数通常是从 randomForest 包继承而来的，因为 caret 包的随机森林方法默认使用的是这个包（所以第一次要安装）。下面是一些可以调整的关键参数：

①mtry: 在每个分割中考虑的变量数量。默认情况下，对于分类问题，mtry 默认值是总变量数的平方根；对于回归问题，是总变量数的三分之一。

②ntree: 构建的树的数量。更多的树可以提高模型的稳定性和准确性，但会增加计算时间和内存使用。默认值通常是 500。

③nodesize: 每个叶节点最少包含的样本数。增加这个参数的值可以减少模型的过拟合，但可能会导致欠拟合。对于分类问题，默认值通常为 1，而回归问题则较大。

④maxnodes: 最大的节点数。这限制了树的最大大小，可以用来控制模型复杂度。

⑤importance: 是否计算变量重要性。这不会影响模型的预测能力，但会影响变量重要性分数的计算。

⑥replace: 是否进行有放回抽样。默认为 TRUE，意味着进行有放回的抽样。

⑦classwt: 类的权重，用于分类问题中的不平衡数据。

⑧cutoff: 分类问题中用于预测类别的概率阈值。这通常是一个类别数目的向量。

⑨sampsize: 用于每棵树的样本大小，如果使用有放回抽样（replace=TRUE），它决定了每个样本被抽样的次数。

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mtry值进行测试，其他默认值。

随机森林祖传的过拟合现象。

三、随机森林调参方法（4个值）

设置mtry值取值2、数据集列数的平方根、数据集列数的一半；ntree取值100、500和1000；nodesize取值1、5、10；maxnodes取值30、50、100：

# Load necessary libraries
library(caret)
library(pROC)
library(ggplot2)
library(randomForest)  # Using randomForest for model fitting

# 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 ranges for each parameter including mtry
mtry_range <- c(2, sqrt(ncol(trainData)), ncol(trainData)/2)
ntree_range <- c(100, 500, 1000)
nodesize_range <- c(1, 5, 10)
maxnodes_range <- c(30, 50, 100)

# Initialize variables to store the best model, its AUC, and parameters
best_auc <- 0
best_model <- NULL
best_params <- NULL  # Initialize best_params to store parameter values

# Nested loops to try different combinations of parameters including mtry
for (mtry in mtry_range) {
  for (ntree in ntree_range) {
    for (nodesize in nodesize_range) {
      for (maxnodes in maxnodes_range) {
        # Train the Random Forest model
        rf_model <- randomForest(X ~ ., data = trainData, mtry=mtry, ntree=ntree, nodesize=nodesize, maxnodes=maxnodes)
        
        # Predict on validation set using probabilities for ROC
        validProb <- predict(rf_model, newdata = validData, type = "prob")[,2]
        
        # Calculate AUC
        validRoc <- roc(validData$X, validProb)
        auc <- auc(validRoc)
        
        # Update the best model if the current model is better
        if (auc > best_auc) {
          best_auc <- auc
          best_model <- rf_model
          best_params <- list(mtry=mtry, ntree=ntree, nodesize=nodesize, maxnodes=maxnodes)  # Store parameters of the best model
        }
      }
    }
  }
}

# Check if a best model was found and output parameters
if (!is.null(best_params)) {
  cat("The best model parameters are:\n")
  print(best_params)
} else {
  cat("No model was found to exceed the baseline performance.\n")
}

# Use best model for predictions
trainPredict <- predict(best_model, trainData, type = "prob")[,2]
validPredict <- predict(best_model, validData, type = "prob")[,2]

# Rest of the analysis, including ROC curves and plotting
trainRoc <- roc(response = trainData$X, predictor = trainPredict)
validRoc <- roc(response = validData$X, predictor = validPredict)

# Plotting code continues unchanged
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")

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)
}

# Function to plot confusion matrix and further analysis remains the same...
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：

还不错，至少过拟合调下来了。

五、最后

数据嘛：

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

提取码：x8xm

阅读全部

相关推荐

  7. 机器学习实验------随机森林

    2024-07-09 20:50:03       30 阅读

最近更新

  3. docker php8.1+nginx base 镜像 dockerfile 配置

    2024-07-09 20:50:03       50 阅读

  4. Could not load dynamic library ‘cudart64_100.dll‘

    2024-07-09 20:50:03       54 阅读

  9. 在Django里面运行非项目文件

    2024-07-09 20:50:03       43 阅读

  19. Python语言-面向对象

    2024-07-09 20:50:03       54 阅读

  20. 如何分清楚常见的 Git 分支管理策略Git Flow、GitHub Flow 和 GitLab Flow

    2024-07-09 20:50:03       50 阅读

热门阅读

  2. 移动校园(8): Uniapp 中路由守卫进行用户权限管理--不同用户不同功能

    2024-07-09 20:50:03       16 阅读

  4. Unity3D 游戏摇杆的制作与实现详解

    2024-07-09 20:50:03       30 阅读

  7. 从零开始！Jupyter Notebook的安装详细教程

    2024-07-09 20:50:03       42 阅读

  9. 论文阅读：Rethinking Interpretability in the Era of Large Language Models

    2024-07-09 20:50:03       25 阅读

  15. MySQL篇五：基本查询

    2024-07-09 20:50:03       19 阅读

  21. 深入Django（七）

    2024-07-09 20:50:03       23 阅读

  25. 【LeetCode】赎金信

    2024-07-09 20:50:03       22 阅读

  26. Pip换源，以及python解耦方法实现

    2024-07-09 20:50:03       22 阅读