Confusion Matrix

A confusion matrix is a table layout that allows visualization of the performance of an algorithm. This kind of assessment is commonly used in the field of machine learning and specifically the problem of statistical classification. The matrix begins with classification accuracy, which is the ratio of correct predictions to total predictions made. Calculating a confusion matrix comes in handy because it gives you a better idea of what your classification model is getting right and what types of errors are occurring. Each row of the confusion matrix represents the instances in an actual class while each column represents the instances in a predicted class, or vice versa.