Evaluating the Model's Performance

Now that we know how to train a Random Forest classifier, it is time to check whether we did a good job or not. What we want is to get a model that makes extremely accurate predictions, so we need to assess its performance using some kind of metric.

For a classification problem, multiple metrics can be used to assess the model's predictive power, such as F1 score, precision, recall, or ROC AUC. Each of them has its own specificity and depending on the projects and datasets, you may use one or another.

In this chapter, we will use a metric called accuracy score. It calculates the ratio between the number of correct predictions and the total number of predictions made by the model:

Figure 4.5: Formula for accuracy score

For instance, if your model made 950 correct predictions out of 1,000 cases, then the accuracy score would be 950/1000 = 0.95. This would mean that your model was 95% accurate on that dataset...