r语言如何使用网格搜索和交叉验证找到合适参数
时间: 2024-01-29 22:03:45 浏览: 37
在R语言中,我们可以使用`caret`包来进行网格搜索和交叉验证。下面是一些简单的步骤:
1. 载入`caret`包
```
library(caret)
```
2. 准备数据集
```
data(iris)
x <- iris[,1:4]
y <- iris[,5]
```
3. 定义模型和参数网格
比如,我们使用支持向量机(SVM)模型,并定义一些可能的参数组合:
```
model <- train(x, y, method = "svmRadial", tuneLength = 3,
preProc = c("center", "scale"),
tuneGrid = expand.grid(sigma = c(0.1, 0.5, 1),
C = c(1, 5, 10)))
```
其中,`method`参数指定了使用的模型类型,`tuneLength`参数指定了交叉验证的折数,`preProc`参数指定了预处理方式,`tuneGrid`参数指定了参数网格。
4. 进行交叉验证和网格搜索
```
fit <- train(x, y, method = "svmRadial", trControl = trainControl(method = "cv", number = 5),
tuneGrid = expand.grid(sigma = c(0.1, 0.5, 1),
C = c(1, 5, 10)),
preProc = c("center", "scale"))
```
其中,`trControl`参数指定了交叉验证的方式,`tuneGrid`参数指定了参数网格。
5. 查看最佳参数和模型性能
```
print(fit)
```
输出类似于:
```
Support Vector Machines with Radial Basis Function Kernel
150 samples
4 predictor
3 classes: 'setosa', 'versicolor', 'virginica'
Pre-processing: centered (4), scaled (4)
Resampling: Cross-Validated (5 fold)
Summary of sample sizes: 120, 120, 120, 120, 120
Resampling results:
sigma C Accuracy Kappa
0.1 1 0.96 0.94
0.1 5 0.96 0.94
0.1 10 0.96 0.94
0.5 1 0.96 0.94
0.5 5 0.96 0.94
0.5 10 0.96 0.94
1.0 1 0.96 0.94
1.0 5 0.96 0.94
1.0 10 0.96 0.94
Accuracy was used to select the optimal model using the largest value.
The final values used for the model were sigma = 0.1 and C = 1.
```
可以看到,最佳参数是`sigma = 0.1`和`C = 1`,模型的准确率为0.96。