R での tidymodels によるモデリング
David Svancer
Data Scientist
すべての yardstick 関数はモデル結果のtibbleを必要とする
yardstick
hwy
.pred
mpg_test_results
# A tibble: 57 x 3 hwy cty .pred <int> <int> <dbl> 1 29 18 25.0 2 31 20 27.7 3 27 18 25.0 4 26 18 25.0 5 25 16 22.3 # ... with 47 more rows
RMSEは平均予測誤差を推定する
rmse()
truth
estimate
mpg_test_results %>% rmse(truth = hwy, estimate = .pred)
# A tibble: 1 x 3 .metric .estimator .estimate <chr> <chr> <dbl> 1 rmse standard 1.93
実測値と予測値の相関の二乗を測る指標
rsq()
mpg_test_results %>% rsq(truth = hwy, estimate = .pred)
# A tibble: 1 x 3 .metric .estimator .estimate <chr> <chr> <dbl> 1 rsq standard 0.904
決定係数（R二乗）の可視化
ggplot2でR二乗プロットを作成
geom_point()
geom_abline()
coord_obs_pred()
ggplot(mpg_test_results, aes(x = hwy, y = .pred)) + geom_point() + geom_abline(color = 'blue', linetype = 2) + coord_obs_pred() + labs(title = 'R-Squared Plot', y = 'Predicted Highway MPG', x = 'Actual Highway MPG')
ggplot(mpg_test_results, aes(x = hwy, y = .pred)) +
geom_point() +
geom_abline(color = 'blue', linetype = 2) +
coord_obs_pred() + labs(title = 'R-Squared Plot', y = 'Predicted Highway MPG', x = 'Actual Highway MPG')
last_fit() 関数
last_fit()
lm_last_fit <- lm_model %>% last_fit(hwy ~ cty, split = mpg_split)
collect_metrics() 関数
collect_metrics()
lm_last_fit %>% collect_metrics()
# A tibble: 2 x 3 .metric .estimator .estimate <chr> <chr> <dbl> 1 rmse standard 1.93 2 rsq standard 0.904
collect_predictions() 関数
collect_predictions()
lm_last_fit %>% collect_predictions()
# A tibble: 57 x 4 id .pred .row hwy <chr> <dbl> <int> <int> 1 train/test split 25.0 1 29 2 train/test split 27.7 3 31 3 train/test split 25.0 7 27 4 train/test split 25.0 8 26 5 train/test split 22.3 9 25 # ... with 47 more rows
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