Model Evaluation Metrics

Let's look at different metrics for evaluating logistic regression models. Evaluating model performance is central for understanding how well our models are generalizing to new data and identifying areas for improvement. In this recipe, we will explore various metrics for evaluating logistic regression models, including precision, recall, F1 score, and ROC curves – metrics and techniques you should already be familiar with from Chapter 4 where we applied them to KNN. We will apply these metrics to assess the performance of our models and learn to interpret the results effectively.

Getting ready

By now you probably have a fairly good idea what we need to do in order to evaluate our models using scikit-learn’s methods.

1. Load the libraries:

   from sklearn.model_selection import train_test_split
   from sklearn.linear_model import LogisticRegression
   from sklearn.metrics import accuracy_score, classification_report, precision_score, recall_score, f1_score...