交叉验证

R 中的树模型机器学习

Sandro Raabe

Data Scientist

k 折交叉验证

将数据分成 k 折

k 折交叉验证

第 1 折作为测试集

k 折交叉验证

第 2 折作为测试集

k 折交叉验证

第 3 折作为测试集

k 折交叉验证

第 4 折作为测试集

k 折交叉验证

第 5 折作为测试集

k 折交叉验证

总交叉验证 MAE

在全量数据上拟合最终模型

编码 - 将数据划分 10 次

# Random seed for reproducibility
set.seed(100)

# Create 10 folds of the dataset
chocolate_folds <- vfold_cv(chocolate_train, v = 10)

#  10-fold cross-validation
# A tibble: 10 x 2
  splits             id
1 <split [1293/144]> Fold1
2 <split [1293/144]> Fold2
3 <split [1293/144]> Fold3
4 ...

编码 - 拟合各折

# Fit a model for every fold and calculate MAE and RMSE
fits_cv <- fit_resamples(tree_spec,

                         final_grade ~ .,

                         resamples = chocolate_folds,

                         metrics = metric_set(mae, rmse))

# Resampling results
# 10-fold cross-validation
# A tibble: 10 x 4
  splits             id    .metrics
  <list>             <chr> <list>
1 <split [1293/144]> Fold1 <tibble [2 x 4]>
2 <split [1293/144]> Fold2 <tibble [2 x 4]>
3 <split [1293/144]> Fold3 <tibble [2 x 4]>
4 ...

编码 - 汇总全部误差

# Collect raw errors of all model runs
all_errors <- collect_metrics(fits_cv, 
                              summarize = FALSE)

print(all_errors)

# A tibble: 20 x 3
   id      .metric  .estimate
   <chr>     <chr>      <dbl>
 1 Fold01      mae      0.362
 2 Fold01     rmse      0.442
 3 Fold02      mae      0.385
 4 Fold02     rmse      0.504
 5 ...

library(ggplot2)
ggplot(all_errors, aes(x = .estimate, 
                       fill = .metric)) +
   geom_histogram()

误差直方图

编码 - 汇总训练结果

# Collect and summarize errors of all model runs
collect_metrics(fits_cv)

# A tibble: 2 x 3
  .metric   mean      n
  <chr>    <dbl>  <int>
1 mae      0.383     10
2 rmse     0.477     10

开始交叉验证！

R 中的树模型机器学习