What is a confusion matrix used for in machine learning?

A confusion matrix is a critical tool in assessing the effectiveness of a classification model. It is a table layout that allows for the visualization of the performance of an algorithm by displaying the actual versus predicted classifications. Key components of a confusion matrix include true positives, true negatives, false positives, and false negatives. These metrics provide insights into the accuracy of a model and its ability to correctly classify instances into various categories.

The values in a confusion matrix allow for the calculation of various performance metrics, such as accuracy, precision, recall, and F1 score, which help determine how well the model is performing. This feedback is essential for model improvement, as it identifies not only the overall effectiveness but also any specific classes that the model may struggle to predict accurately.

In contrast, the other options do not accurately describe the purpose of a confusion matrix. Summarizing a dataset and visualizing data pertain to different aspects of data analysis, while feature scaling is concerned with normalizing the range of independent variables in a dataset rather than evaluating classification performance.