Example of output from a logistic regression model.

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Model evaluation in logistic regression helps assess how well our model performs in classifying binary outcomes.

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It involves analysing various metrics and visualisation techniques to understand the model's strengths, weaknesses, and overall predictive capability.

The key aspects of model evaluation include:

• Performance Metrics: Using various measures derived from the confusion matrix to quantify model accuracy and reliability.
• Threshold Analysis: Examining how different classification thresholds affect model performance through ROC curves and precision-recall trade-offs.
• Error Analysis: Understanding the types of mistakes the model makes (false positives vs. false negatives) and their implications.

The following sections detail the essential tools and metrics used in model evaluation:

Content map

To learn more about model evaluation, read each of the following sections:

Confusion Matrix

ROC Curve and AUC

Precision and Recall

Next element

Feature engineering