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  3. Supervised Learning in R: Classification

Exercise

Making a binary prediction

In the previous exercise, you used the glm() function to build a logistic regression model of donor behavior. As with many of R's machine learning methods, you can apply the predict() function to the model object to forecast future behavior. By default, predict() outputs predictions in terms of log odds unless type = "response" is specified. This converts the log odds to probabilities.

Because a logistic regression model estimates the probability of the outcome, it is up to you to determine the threshold at which the probability implies action. One must balance the extremes of being too cautious versus being too aggressive. For example, if you were to solicit only the people with a 99% or greater donation probability, you may miss out on many people with lower estimated probabilities that still choose to donate. This balance is particularly important to consider for severely imbalanced outcomes, such as in this dataset where donations are relatively rare.

The dataset donors and the model donation_model are available for you to use.

Instructions

100 XP
  • Use the predict() function to estimate each person's donation probability. Use the type argument to get probabilities. Assign the predictions to a new column called donation_prob.
  • Find the actual probability that an average person would donate by passing the mean() function the appropriate column of the donors data frame.
  • Use ifelse() to predict a donation if their predicted donation probability is greater than average. Assign the predictions to a new column called donation_pred.
  • Use the mean() function to calculate the model's accuracy.