衡量交叉验证性能

Tidyverse 中的机器学习

Dmitriy (Dima) Gorenshteyn

Lead Data Scientist, Memorial Sloan Kettering Cancer Center

性能评估

性能评估——真实值

性能评估——预测

性能评估

平均绝对误差（MAE）

性能评估的要素

1) 实际的 life_expectancy 值
2) 预测的 life_expectancy 值
3) 比较 1) 与 2) 的度量

1) 提取实际值

cv_prep_lm <- cv_models_lm %>% 
  mutate(validate_actual = map(validate, ~.x$life_expectancy))

predict() 与 map2() 函数

predict(model, data)

map2(.x = model, .y = data, .f = ~predict(.x, .y))

2) 准备预测值

cv_prep_lm <- cv_eval_lm %>% 
  mutate(validate_actual = map(validate, ~.x$life_expectancy),
         validate_predicted = map2(model, validate, ~predict(.x, .y)))

3) 计算 MAE

library(Metrics)
cv_eval_lm <- cv_prep_lm %>% 
  mutate(validate_mae = map2_dbl(validate_actual, validate_predicted, 
                                ~mae(actual = .x, predicted = .y)))

cv_eval_lm

#  5-fold cross-validation 
# A tibble: 5 x 8
splits       id    train validate model validate_a. validate_p validate_mae
<S3: rsplit> Fold1 <tib. <tib.   <S3.   <dbl.       <dbl.       1.47
<S3: rsplit> Fold2 <tib. <tib.   <S3.   <dbl.       <dbl.       1.51
<S3: rsplit> Fold3 <tib. <tib.   <S3.   <dbl.       <dbl.       1.44
<S3: rsplit> Fold4 <tib. <tib.   <S3.   <dbl.       <dbl.       1.48
<S3: rsplit> Fold5 <tib. <tib.   <S3.   <dbl.       <dbl.       1.68

¡Vamos a practicar!

Tidyverse 中的机器学习