Mastering Predictive Analytics with scikit-learn and TensorFlow

Training different regression models

The following screenshot shows a dataframe where we are going to save performance. We are going to run four models, namely logistic regression, bagging, random forest, and boosting:

We are going to use the following evaluation metrics in this case:

• accuracy: This metric measures how often the model predicts defaulters and non-defaulters correctly
• precision: This metric will be when the model predicts the default and how often the model is correct
• recall: This metric will be the proportion of actual defaulters that the model will correctly predict

The most important of these is the recall metric. The reason ...