CommencerCommencer gratuitement

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.

Cet exercice fait partie du cours

Machine Learning with caret in R

Afficher le cours

Instructions

  • 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.

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

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


# Convert to factor: p_class


# Create confusion matrix
Modifier et exécuter le code