Construindo e ajustando um modelo de random forest

Machine Learning no tidyverse

Dmitriy (Dima) Gorenshteyn

Lead Data Scientist, Memorial Sloan Kettering Cancer Center

Desempenho da validação cruzada

Modelo de Regressão Linear

Validar Erro Absoluto Médio:

1,5 anos

Outro modelo

Vantagens do Random Forest

Lida com relações não lineares
Lida com interações

Ferramentas básicas de Random Forest

Modelo

rf_model <- ranger(formula = ___, data = ___, seed = ___)

Predição

prediction <- predict(rf_model, new_data)$predictions

Construir modelos básicos de Random Forest

library(ranger)
cv_models_rf <- cv_data %>% 
 mutate(model = map(train, ~ranger(formula = life_expectancy~., 
                                    data = .x, seed = 42)))

cv_prep_rf <- cv_models_rf %>% 
 mutate(validate_predicted = map2(model, validate, 
                                  ~predict(.x, .y)$predictions))

Hiperparâmetros do ranger

Modelo

rf_model <- ranger(formula, data, seed, mtry, num.trees)

Hiperparâmetros

name	range	default
mtry	$1:number\ of\ features$	$\sqrt{number\ of\ feat}$
num.trees	$1:\infty$	$500$

Ajustar os hiperparâmetros

cv_tune <- cv_data %>% 
  crossing(mtry = 1:5)

cv_tune

# A tibble: 25 x 5
   splits       id    train                validate            mtry
   <list>       <chr> <list>               <list>             <int>
 1 <S3: rsplit> Fold1 <tibble [2,402 × 7]> <tibble [601 × 7]>     1
 2 <S3: rsplit> Fold1 <tibble [2,402 × 7]> <tibble [601 × 7]>     2
 3 <S3: rsplit> Fold1 <tibble [2,402 × 7]> <tibble [601 × 7]>     3
 4 <S3: rsplit> Fold1 <tibble [2,402 × 7]> <tibble [601 × 7]>     4
 5 <S3: rsplit> Fold1 <tibble [2,402 × 7]> <tibble [601 × 7]>     5
 6 <S3: rsplit> Fold2 <tibble [2,402 × 7]> <tibble [601 × 7]>     1
 7 <S3: rsplit> Fold2 <tibble [2,402 × 7]> <tibble [601 × 7]>     2

Ajustar os hiperparâmetros

cv_model_tunerf <- cv_tune %>% 
  mutate(model = map2(train, mtry, ~ranger(formula = life_expectancy~., 
                                           data = .x, mtry = .y)))

cv_model_tunerf

# A tibble: 25 x 6
   splits       id    train                validate      mtry  model       
 * <list>       <chr> <list>               <list>        <int> <list>      
 1 <S3: rsplit> Fold1 <tibble [2,402 × 7]> <tibble [60…   1    <S3: ranger>
 2 <S3: rsplit> Fold1 <tibble [2,402 × 7]> <tibble [60…   2    <S3: ranger>
 3 <S3: rsplit> Fold1 <tibble [2,402 × 7]> <tibble [60…   3    <S3: ranger>
 4 <S3: rsplit> Fold1 <tibble [2,402 × 7]> <tibble [60…   4    <S3: ranger>
 5 <S3: rsplit> Fold1 <tibble [2,402 × 7]> <tibble [60…   5    <S3: ranger>
 6 <S3: rsplit> Fold2 <tibble [2,402 × 7]> <tibble [60…   1    <S3: ranger>
 7 <S3: rsplit> Fold2 <tibble [2,402 × 7]> <tibble [60…   2    <S3: ranger>

Vamos praticar!

Machine Learning no tidyverse