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  3. Supervised Learning in R: Classification

Exercise

Building a more sophisticated model

One of the best predictors of future giving is a history of recent, frequent, and large gifts. In marketing terms, this is known as R/F/M:

  • Recency
  • Frequency
  • Money

Donors that haven't given both recently and frequently may be especially likely to give again; in other words, the combined impact of recency and frequency may be greater than the sum of the separate effects.

Because these predictors together have a greater impact on the dependent variable, their joint effect must be modeled as an interaction. The donors dataset has been loaded for you.

Instructions

100 XP
  • Create a logistic regression model of donated as a function of money plus the interaction of recency and frequency. Use * to add the interaction term.
  • Examine the model's summary() to confirm the interaction effect was added.
  • Save the model's predicted probabilities as rfm_prob. Use the predict() function, and remember to set the type argument.
  • Plot a ROC curve by using the function roc(). Remember, this function takes the column of outcomes and the vector of predictions.
  • Compute the AUC for the new model with the function auc() and compare performance to the simpler model.