classification report

A classification report is a summary used to evaluate how well a machine learning model distinguishes between different categories in a dataset. It provides key metrics such as precision (how many of the predicted positives are correct), recall (how many actual positives were correctly identified), and the F1 score (a balance between precision and recall). These metrics help you understand the model’s accuracy and effectiveness in correctly classifying items, offering a clear picture of its strengths and areas for improvement in specific categories.