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.
The following Colab file (see the Evaluating the model's effectiveness section in the following repository) contains the code referred to in this recipe:
preparing_model.ipynb:
Getting ready
To complete this recipe, we need to know what a confusion matrix is and which performance metrics we can use to understand whether...
