Hands-On Python Deep Learning for the Web

Model evaluation

We have trained an ML model but how well will the model perform on the data it has never seen before? We answer this question using model evaluation. 

Different machine learning algorithms call for different evaluation metrics. 

For supervised learning methods, we usually use the following:

• The confusion matrix, which is a matrix consisting of four values: True Positive, False Positive, True Negative, and False Negative
• Accuracy, precision, recall, and F1-score (these are all byproducts of the confusion matrix)
• The Receiver Operator Characteristic (ROC) curve and the Area Under Curve (AUC) metric
• R-square (coefficient of determination), Root Mean Square Error (RMSE), F-statistic, Akaike Information Criterion (AIC), and p-values ...