Evaluating the model’s effectiveness
Accuracy and loss are not enough to judge the model’s effectiveness. In general, accuracy is a good performance indicator if the dataset is balanced, but it does not tell us the strengths and weaknesses of our model. For instance, what classes do we recognize with high confidence? What frequent mistakes does the model make?
This recipe will judge the model’s effectiveness by visualizing the confusion matrix and evaluating the recall, precision, and F1-score performance metrics.
Getting ready
To complete this recipe, we must familiarize ourselves with the confusion matrix and the alternative performance metrics crucial for evaluating the model’s effectiveness. Let’s start by learning the confusion matrix in the following subsection.
Evaluating the performance with the confusion matrix
A confusion matrix is an NxN matrix reporting the number...
