Prediktera och utvärdera

Maskininlärning med trädbaserade modeller i R

Sandro Raabe

Data Scientist

Prediktera på ny data

predict(model, new_data, type)

predict(model, new_data = test_data, 
               type = "class")

  .pred_class
  <fct>      
1 no         
2 no         
3 yes        
4 no

predict(model, new_data = test_data, 
               type = "prob")

     .pred_no  .pred_yes
     <dbl>     <dbl>
1    0.866     0.134
2    0.956     0.044
3    0.672     0.328
4    0.877     0.123

confusion_matrix_1

confusion_matrix_2

confusion_matrix_3

confusion_matrix_4

confusion_matrix_4

# Combine predictions and truth values
pred_combined <- predictions %>% 
   mutate(true_class = test_data$outcome)

pred_combined

  .pred_class  true_class
  <fct>        <fct>     
1 no           no        
2 no           yes       
3 no           no        
4 yes          yes

# Calculate the confusion matrix
conf_mat(data = pred_combined,

         estimate = .pred_class,

         truth = true_class)

             Truth
Prediction    no   yes
        no   116    31
       yes    12    40

$$\text{accuracy} = \frac{\text{n of correct predictions}}{\text{n of total predictions}}$$

Funktionsnamn: accuracy()
Samma argument som conf_mat()
- data, estimate och truth
- Gemensam struktur i yardstick

accuracy(pred_combined,
         estimate = .pred_class,
         truth = true_class)

  .metric     .estimate
  <chr>           <dbl>
1 accuracy        0.708

Maskininlärning med trädbaserade modeller i R