What is the purpose of a confusion matrix in machine learning?

A confusion matrix serves the essential function of evaluating the performance of classification models in machine learning by systematically comparing the predicted outcomes with the actual outcomes. This matrix presents a summarized view of the classification results, displaying true positives, true negatives, false positives, and false negatives.

By analyzing these different components, one can derive several important performance metrics, such as accuracy, precision, recall, and F1 score. This detailed breakdown allows practitioners to understand not only how well the model is performing but also where it might be making mistakes, which is crucial for refining and optimizing the model further.

Other options presented do not align with the purpose of a confusion matrix. For example, data cleaning processes focus on preparing data for analysis rather than evaluating model performance. Visualizing the distribution of datasets does not make use of a confusion matrix but rather employs different visualization techniques. Calculating runtime pertains to performance efficiency of algorithms, which is unrelated to the evaluation of prediction accuracy. Therefore, analyzing a confusion matrix is a fundamental step in the assessment and improvement of classification models in machine learning.