Overview
Model evaluation is a critical step in the machine learning process that helps determine how well a model performs on unseen data. By using various metrics such as accuracy, precision, recall, and F1 score, data scientists can gain insights into the strengths and weaknesses of their models. Understa...
Key Terms
Example: If a model predicts 80 out of 100 instances correctly, its accuracy is 80%.
Example: If a model predicts 10 positives and 8 are correct, precision is 80%.
Example: If there are 10 actual positives and the model identifies 8, recall is 80%.
Example: An F1 score of 0.8 indicates a good balance between precision and recall.
Example: It shows true positives, false positives, true negatives, and false negatives.
Example: An overfitted model performs well on training data but poorly on unseen data.