Model Evaluation
ICSE · Class 12 · Artificial Intelligence
Quick revision notes for Model Evaluation — ICSE Class 12 Artificial Intelligence. Key concepts, formulas, and definitions for last-minute revision.
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Key Topics to Revise
1. Evaluation and Testing Data
- Evaluation checks the reliability and performance of an AI model.
- Testing data must be used for evaluation, not training data.
- Using training data for evaluation can lead to overfitting.
2. Confusion Matrix and Classification Outcomes
- Classification is a type of supervised learning task.
- Binary classification has two classes, such as yes/no or positive/negative.
- Multi-class classification has more than two classes.
3. Accuracy, Precision, Recall, and F1 Score
- Accuracy measures the percentage of correct predictions out of total predictions.
- High accuracy can be misleading in class-imbalanced datasets.
- In the forest fire example, always predicting no fire gives 98% accuracy but is useless.
4. Metric Choice Based on Error Cost
- The best metric depends on the problem and the cost of errors.
- Forest fire and viral outbreak scenarios have high False Negative cost.
- Mining and spam detection scenarios have high False Positive cost.
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