Confusion matrix

A confusion matrix is a tool used to help understand how well a machine learning algorithm is doing. It's a table of numbers that shows how many times the algorithm got things right and how many times it got things wrong. The table shows the actual outcomes (in the columns) compared to the predicted outcomes (in the rows). Each column represents a different predicted outcome, such as “yes” or “no”, and each row represents a different actual outcome, such as “true” or “false”. The numbers in the table tell us how many times the algorithm predicted the correct outcome, how many times it predicted an incorrect outcome, and how accurate the algorithm was overall.