插入符号中的PCA阈值调整

问题描述

我正在尝试使用插入符号从某些数据构建分类器。 我想尝试的方法之一是使用PCA预处理的数据中的简单LDA。 我发现了如何使用插入符号：

fitControl <- trainControl("repeatedcv",number=10,repeats = 10,preProcoptions = list(thresh = 0.9))
ldaFit1 <- train(label ~ .,data = tab,method = "lda2",preProcess = c("center","scale","pca"),trControl = fitControl)

如预期的插入符号所示，我们将LDA的精度与不同尺寸值进行了比较：

Linear discriminant Analysis

 158 samples
1955 predictors
   3 classes: '1','2','3'

Pre-processing: centered (1955),scaled (1955),principal component
 signal extraction (1955)
resampling: Cross-Validated (10 fold,repeated 10 times)
Summary of sample sizes: 142,142,143,...
resampling results across tuning parameters:

  dimen  Accuracy   Kappa
  1      0.5498987  0.1151681
  2      0.5451340  0.1298590

Accuracy was used to select the optimal model using the largest value.
The final value used for the model was dimen = 1.

我想做的是将PCA阈值添加到调整参数中，但是我找不到解决方法。

使用插入符号是否有一个简单的解决方案？还是需要用不同的预处理选项重复训练步骤并最终选择最佳值？

解决方法

由于误用指出的链接，我设法将PCA的方差解释阈值集成到参数调整中：

library(caret)
library(recipes)
library(MASS)

# Setting up a vector of thresholds to try out
pca_varex <- c(0.8,0.9,0.95,0.97,0.98,0.99,0.995,0.999)

# Setting up recipe
initial_recipe <- recipe(train,formula = label ~ .) %>%
                    step_center(all_predictors()) %>%
                    step_scale(all_predictors())

# Define the modelgrid
models <- model_grid() %>%
            share_settings(data = train,trControl = caret::trainControl(method = "repeatedcv",number = 10,repeats = 10),method = "lda2") 

# Add models with different PCA thresholds
for (i in pca_varex) {
    models <- models %>% add_model(model_name = sprintf("varex_%s",i),x = initial_recipe %>%
                                        step_pca(all_predictors(),threshold = i))
}

# Train
models <- models %>% train(.)

尽管通过查找modelgrid和配方文档，tidymodels包可能是最简单的方法（https://www.tidymodels.org/）。