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.
Bu egzersiz
Supervised Learning in R: Classification
kursunun bir parçasıdırEgzersiz talimatları
- Create a logistic regression model of
donatedas a function ofmoneyplus the interaction ofrecencyandfrequency. 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 thepredict()function, and remember to set thetypeargument. - 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.
Uygulamalı interaktif egzersiz
Bu örnek kodu tamamlayarak bu egzersizi bitirin.
# Build a recency, frequency, and money (RFM) model
rfm_model <- ___
# Summarize the RFM model to see how the parameters were coded
# Compute predicted probabilities for the RFM model
rfm_prob <- ___
# Plot the ROC curve and find AUC for the new model
library(pROC)
ROC <- ___
plot(___, col = "red")
auc(___)