Performance of a single model

Now that you have the binary vectors for the actual and predicted values of the model, you can calculate many commonly used binary classification metrics. In this exercise you will focus on:

accuracy: rate of correctly predicted values relative to all predictions.
precision: portion of predictions that the model correctly predicted as TRUE.
recall: portion of actual TRUE values that the model correctly recovered.

This exercise is part of the course

Machine Learning in the Tidyverse

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Exercise instructions

Use table() to compare the validate_actual and validate_predicted values for the example model and validate data frame.
Calculate the accuracy.
Calculate the precision.
Calculate the recall.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

library(Metrics)

# Compare the actual & predicted performance visually using a table
table(___, ___)

# Calculate the accuracy
accuracy(___, ___)

# Calculate the precision
precision(___, ___)

# Calculate the recall
recall(___, ___)

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