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  3. Supervised Learning in R: Regression

Exercise

The gain curve to evaluate the unemployment model

In the previous exercise you made predictions about female_unemployment and visualized the predictions and the residuals. Now, you will also plot the gain curve of the unemployment_model's predictions against actual female_unemployment using the WVPlots::GainCurvePlot() function.

For situations where order is more important than exact values, the gain curve helps you check if the model's predictions sort in the same order as the true outcome.

Calls to the function GainCurvePlot() look like: 

GainCurvePlot(frame, xvar, truthvar, title)

where

  • frame is a data frame
  • xvar and truthvar are strings naming the prediction and actual outcome columns of frame
  • title is the title of the plot

When the predictions sort in exactly the same order, the relative Gini coefficient is 1. When the model sorts poorly, the relative Gini coefficient is close to zero, or even negative.

The data frame unemployment, which also contains the predictions, and the model unemployment_model are available for you to use.

Instructions

100 XP
  • Load the package WVPlots using library().
  • Plot the gain curve. Give the plot the title "Unemployment model". Do the model's predictions sort correctly?