Predictions of a single model
To calculate the performance of a classification model you need to compare the actual values of Attrition to those predicted by the model.
When calculating metrics for binary classification tasks (such as precision and recall), the actual and predicted vectors must be converted to binary values.
In this exercise, you will learn how to prepare these vectors using the model and validate data frames from the first cross-validation fold as an example.
Cet exercice fait partie du cours
Machine Learning in the Tidyverse
Instructions
- Extract the
modeland thevalidatedata frame from the first fold of the cross-validation. - Extract the
Attritioncolumn from thevalidatedata frame and convert the values to binary (TRUE/FALSE). - Use
modelto predict the probabilities of attrition for thevalidatedata frame. - Convert the predicted probabilities to a binary vector, assume all probabilities greater than
0.5are TRUE.
Exercice interactif pratique
Essayez cet exercice en complétant cet exemple de code.
# Extract the first model and validate
model <- cv_models_lr$___[[___]]
validate <- cv_models_lr$___[[___]]
# Prepare binary vector of actual Attrition values in validate
validate_actual <- ___ == "Yes"
# Predict the probabilities for the observations in validate
validate_prob <- predict(___, ___, type = "response")
# Prepare binary vector of predicted Attrition values for validate
validate_predicted <- ___