在不调整 R 的情况下使用 Caret 训练模型

Question

似乎在 caret 中训练模型时，您几乎被迫进行参数调整。我知道这通常是个好主意，但如果我想在训练时明确说明模型参数怎么办？

svm.nf <- train(y ~ .,
                data = nf,
                method = "svmRadial",
                C = 4, sigma = 0.25, tuneLength = 0)

出了点问题；缺少所有 RMSE 指标值：

    RMSE        Rsquared  
 Min.   : NA   Min.   : NA  
 1st Qu.: NA   1st Qu.: NA  
 Median : NA   Median : NA  
 Mean   :NaN   Mean   :NaN  
 3rd Qu.: NA   3rd Qu.: NA  
 Max.   : NA   Max.   : NA  
 NA's   :2     NA's   :2

Error in train.default(x, y, weights = w, ...) : Stopping In addition: Warning message: In nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo, : There were missing values in resampled performance measures.

Answer 1

我想出了一个办法，有点晦涩。您必须创建一个传递给 tuneGrid 的调整参数数据框，每个参数只列出 1 个值。

params <- data.frame(C = 4,sigma=.25)

> params
  C sigma
1 4  0.25

svm.nf <- train(Point_diff ~ .,
              data = nf,
              method = "svmRadial",
              tuneGrid=params)
> svm.nf
Support Vector Machines with Radial Basis Function Kernel 

1248 samples
14 predictor

No pre-processing
Resampling: Bootstrapped (25 reps) 
Summary of sample sizes: 1248, 1248, 1248, 1248, 1248, 1248, ... 
Resampling results:

  RMSE      Rsquared 
  15.53451  0.0550965

Tuning parameter 'sigma' was held constant at a value of 0.25
Tuning parameter 'C'was held constant at a value of 4

在不调整 R 的情况下使用 Caret 训练模型

Training Models in Caret Without Tuning R

r

data-mining

r-caret