Overview
Model accuracy assessment is essential for evaluating how well predictive models perform. It involves various metrics such as accuracy, precision, recall, and the F1 score, each providing unique insights into model performance. Understanding these metrics helps in selecting the right model for speci...
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 predicts 8 correctly, recall is 80%.
Example: An F1 score of 0.8 indicates a good balance between precision and recall.
Example: A confusion matrix shows true positives, false positives, true negatives, and false negatives.
Example: If a model correctly identifies 5 spam emails, that’s 5 true positives.