Overview
Evaluating model quality is a critical step in the data science process, ensuring that predictive models are reliable and effective. By using various metrics such as accuracy, precision, recall, and F1 score, data scientists can assess how well their models perform and make informed decisions about ...
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: K-fold cross-validation splits the data into k subsets and trains the model k times.