用R语言对iris数据集中除Species之外的变量进行核主成分分析,选取6个主成分为输入,Species为输出, 随机选取100个样本为训练集,剩余50个样本为测试集,运用逻辑回归,朴素贝叶斯,knn,支持向量机, 决策树、随机森林和Adaboost方法进行分类,输出混淆矩阵
时间: 2024-10-09 11:08:03 浏览: 49
在R语言中,首先你需要安装并加载必要的库,如`caret`, `tidyverse`, 和 `e1071`(用于SVM)。然后按照以下步骤进行:
1. **加载数据并预处理**:
```r
library(tidyverse)
library(caret)
data(iris)
# 将数据集分为因子(Species)和其他数值变量
df <- iris[, -5]
species_factors <- iris$Species
# 对数值变量进行标准化或归一化
df_scaled <- scale(df)
```
2. **主成分分析(PCA)**:
```r
pca <- prcomp(df_scaled, center = TRUE, scale. = TRUE) # 使用中心化和标准化
summary(pca) # 查看主成分的方差贡献率,选择前6个主成分
# 取前6个主成分
df_pca <- as.data.frame(cbind(pca$x[, 1:6], species_factors))
```
3. **划分数据集**:
```r
set.seed(123) # 为了保证结果可重复
trainIndex <- createDataPartition(df_pca$Species, p = .8, list = FALSE)
train_data <- df_pca[trainIndex, ]
test_data <- df_pca[-trainIndex, ]
```
4. **模型训练与预测**:
```r
# 逻辑回归 (using glm)
logistic_model <- glm(Species ~ ., data = train_data, family = "binomial")
predictions_logistic <- predict(logistic_model, newdata = test_data, type = "response")
# 朴素贝叶斯 (using e1071's naiveBayes)
library(e1071)
nb_model <- naiveBayes(Species ~ ., data = train_data)
predictions_nb <- predict(nb_model, test_data)
# 其他算法,按需添加
# knn (KNN from caret)
knn_model <- train(Species ~ ., method = "knn", data = train_data, trControl = trainControl(method = "cv", number = 10), tuneGrid = expand.grid(k = seq(1, 20)))
predictions_knn <- predict(knn_model, test_data)
# SVM (linear kernel)
svm_model <- svm(Species ~ ., data = train_data, kernel = "linear")
predictions_svm <- predict(svm_model, test_data)
# 决策树 (using rpart)
tree_model <- rpart(Species ~ ., data = train_data, method = "class")
predictions_tree <- predict(tree_model, test_data, type = "class")
# 随机森林 (using randomForest)
random_forest_model <- predict(random_forest_model, test_data)
# Adaboost (using adaBoost package)
if (!requireNamespace("adaBoost", quietly = TRUE)) install.packages("adaBoost")
library(adaBoost)
boost_model <- adaBoost(Class ~ ., data = train_data, n.trees = 100)
predictions_ab <- predict(boost_model, test_data)
```
5. **混淆矩阵生成**:
```r
# 创建混淆矩阵
confusionMatrix(predictions_logistic, test_data$Species)
```
请根据实际操作过程中遇到的问题调整代码,如果需要其他算法的混淆矩阵,请按上述方法替换相应模型。