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Exercise

Creating custom metric sets

The yardstick package also provides the ability to create custom sets of model metrics. In cases where the cost of obtaining false negative errors is different from the cost of false positive errors, it may be important to examine a specific set of performance metrics.

Instead of calculating accuracy, sensitivity, and specificity separately, you can create your own metric function that calculates all three at the same time.

In this exercise, you will use the results from your logistic regression model, telecom_results, to calculate a custom set of performance metrics. You will also use a confusion matrix to calculate all available binary classification metrics in tidymodelsall at once.

The telecom_results tibble has been loaded into your session.

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  • Create a custom metric function named telecom_metrics using the appropriate yardstick function.
    • Include the accuracy(), sens(), and spec() functions in your custom metric function.