Confusion matrix

Machine Learning dengan caret di R

Zach Mayer

Data Scientist at DataRobot and co-author of caret

Confusion matrix

Matriks kebingungan antara prediksi dan referensi. Saat prediksi dan referensi sama-sama "yes", itu true positive. Saat keduanya "no", itu true negative. Saat prediksi "yes" dan referensi "no", itu false positive. Saat prediksi "no" dan referensi "yes", itu false negative.

Confusion matrix

# Fit a model
model <- glm(Class ~ ., family = binomial(link = "logit"), train)
p <- predict(model, test, type = "response")
summary(p)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.0000  0.9885  0.5296  1.0000  1.0000

# Turn probabilities into classes and look at their frequencies
p_class <- ifelse(p > 0.50, "M", "R")
table(p_class)
p_class

 M  R 
44 39

Confusion matrix

Buat tabel frekuensi 2 arah
Bandingkan kelas prediksi vs. aktual

# Make simple 2-way frequency table
table(p_class, test[["Class"]])

p_class  M  R
      M 13 31
      R 30  9

Confusion matrix

# Use caret’s helper function to calculate additional statistics
confusionMatrix(p_class, test[["Class"]])

         Reference
Prediction  M  R
         M 13 31
         R 30  9

               Accuracy : 0.2651          
                 95% CI : (0.1742, 0.3734)
    No Information Rate : 0.5181          
    P-Value [Acc > NIR] : 1               

                  Kappa : -0.4731         
 Mcnemar's Test P-Value : 1               

            Sensitivity : 0.3023          
            Specificity : 0.2250          
         Pos Pred Value : 0.2955          
         Neg Pred Value : 0.2308

Ayo berlatih!

Machine Learning dengan caret di R