Confusion Matrix, Precision, and Recall Explained - KDnuggets

A confusion matrix is a table used to summarize the performance of a classification model. In case you aren't familiar, classification models are machine learning algorithms used to solve problems that have a categorical outcome, such as predicting whether an email is a spam or not. Accuracy is the most popular metric used to evaluate classification models. However, it isn't always the most reliable, which is why data scientists generate confusion matrices and use metrics like precision and recall instead. Confusion matrices are one of the most frequently tested concepts by data science interviewers. Hiring managers often ask candidates to interpret confusion matrices, or provide them with a use case and ask them to calculate a model's precision and recall by hand.