Hands-On Exploratory Data Analysis with Python

Model evaluation

Based on the performance of the test data, we can create a confusion matrix. This matrix contains four different parameters: true positive, true negative, false positive, and false negative. Consider the following confusion matrix:

Predicted: Positive	Predicted: Negative
Actual: Positive	True Positive (TP)	False Negative (FN)
Actual: Negative	False Positive (FP)	True Negative (TN)

This matrix shows four distinct parameters:

• True positives: The model predicts TRUE when the actual value is TRUE.
• True negatives: The model predicts FALSE when the actual value is FALSE.
• False-positives: The model predicts TRUE when the actual value is FALSE. This is also referred to as a Type I Error.
• False-negatives ...