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Exercise

Confusion matrix

When the response variable has just two outcomes, like the case of churn, the measures of success for the model are "how many cases where the customer churned did the model correctly predict?" and "how many cases where the customer didn't churn did the model correctly predict?". These can be found by generating a confusion matrix and calculating summary metrics on it. A mosaic plot is the natural way to visualize the results.

churn and mdl_churn_vs_both_inter are available; dplyr and yardstick are loaded.

Instructions 1/2

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  • Get the actual responses from the churn dataset.
  • Get the predicted responses from the rounded, fitted values of mdl_churn_vs_both_inter.
  • Create a table of the actual and predicted response values.
  • Convert the table to a conf_mat confusion matrix object.