Build final classification model

Comparing the recall performance between the logistic regression model (0.4) and the best performing random forest model (0.2), you've learned that the model with the best performance is the logistic regression model. In this exercise, you will build the logistic regression model using all of the train data and you will prepare the necessary vectors for evaluating this model's test performance.

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Machine Learning in the Tidyverse

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Istruzioni dell'esercizio

Build a logistic regression model predicting Attrition using all available features in the training_data.
Prepare the binary vector of actual test values, test_actual.
Prepare the binary vector of predicted values where a probability greater than 0.5 indicates TRUE and store this as test_predicted.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Build the logistic regression model using all training data
best_model <- glm(formula = ___, 
                  data = ___, family = "binomial")


# Prepare binary vector of actual Attrition values for testing_data
test_actual <- testing_data$___ == "___"

# Prepare binary vector of predicted Attrition values for testing_data
test_predicted <- predict(___, ___, type = "response") > ___

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