BaşlayınÜcretsiz Başlayın

From probabilites to confusion matrix

Conversely, say you want to be really certain that your model correctly identifies all the mines as mines. In this case, you might use a prediction threshold of 0.10, instead of 0.90.

The code pattern for cutting probabilities into predicted classes, then calculating a confusion matrix, was shown in Exercise 7 of this chapter.

Bu egzersiz

Machine Learning with caret in R

kursunun bir parçasıdır
Kursu Görüntüle

Egzersiz talimatları

  • Use ifelse() to create a character vector, m_or_r that is the positive class, "M", when p is greater than 0.1, and the negative class, "R", otherwise.
  • Convert m_or_r to be a factor, p_class, with levels the same as those of test[["Class"]].
  • Make a confusion matrix with confusionMatrix(), passing p_class and the "Class" column from the test dataset.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# If p exceeds threshold of 0.1, M else R: m_or_r


# Convert to factor: p_class


# Create confusion matrix
Kodu Düzenle ve Çalıştır