What is the purpose of a confusion matrix?

A confusion matrix is a powerful tool used in evaluating the performance of a classification model. Its primary purpose is to compare predicted results against actual results, allowing a clear visualization of how well the model is performing in correctly classifying instances within each category.

The matrix presents counts of true positives, true negatives, false positives, and false negatives, which facilitates the calculation of key performance metrics such as accuracy, precision, recall, and F1 score. By summarizing these counts, the confusion matrix provides insights into specific areas where the model might be making errors, thus helping practitioners to refine the model and improve its predictive capabilities.

In contrast, the other options represent different concepts within the realm of data science. Visualizing the distribution of data pertains to exploratory data analysis and is not the focus of a confusion matrix. Outlining steps in a data pipeline relates to the process of data preparation and transformation, which is distinct from model evaluation. Enhancing a model's interpretability generally involves techniques such as feature importance analysis or SHAP values, rather than the direct comparison of predicted and actual outcomes.