What is the purpose of a confusion matrix?

A confusion matrix serves the primary purpose of summarizing the predictive performance of a classification model. It provides a comprehensive overview of the model's classification results, showcasing the number of correct and incorrect classifications across different classes. This matrix typically includes four key components: true positives, true negatives, false positives, and false negatives.

By analyzing these values, you can calculate various performance metrics, such as accuracy, precision, recall, and F1-score, which help in understanding how well the model is performing. The insights gained from a confusion matrix are crucial for evaluating classifiers, allowing practitioners to identify areas where the model may be misclassifying data.

The other options, while relevant in the context of machine learning and model evaluation, do not align with the specific function of a confusion matrix. Visualizing the distribution of input data refers to techniques like histograms or scatter plots, determining feature importance involves assessing which variables have the most influence on the model's predictions, and tracking changes in model performance over time typically involves monitoring various metrics or employing tools that support model auditing and versioning. These aspects are important in their own right but are distinct from the primary role of a confusion matrix in model evaluation.