When developing predictive models, it is important to know how to evaluate those models. In this section, we are going to discuss five different ways to evaluate the performance of classification models. The first metric that can be used to measure prediction performance is accuracy. Accuracy is simply the percentage of correct predictions out of all predictions, as shown in the following formula:
The second metric that is commonly used for classification problems is precision. Precision is defined as the number of true positives divided by the total number of true positives and false positives. True positives are cases where the model correctly predicted as positive, while false positives are cases where the model was predicted as positive, but the true label was negative. The formula looks as follows:
Along with precision, recall is also commonly...
