Think Different

                                                                           Photo by Ann H Model evaluation is a crucial step in the machine learning lifecycle to assess how well a trained model performs on unseen data. Different evaluation techniques provide insights into various aspects of a model's performance. Here are some common model evaluation techniques along with brief explanations and examples: 1. Confusion Matrix:    - Explanation: A confusion matrix is a table that describes the performance of a classification model. It shows the number of True Positives (TP), True Negatives (TN), False Positives (FP), and False Negatives (FN).    - Example:      ```                    ...