Chapter 8: Model Evaluation Metrics
Evaluation metrics are crucial for assessing the performance of classification models. Various metrics such as confusion matrix, accuracy, precision, recall, F1 score, and ROC curve provide insights into a model's effectiveness, especially in cases where data may be imbalanced. Understanding and applying these metrics ensures a comprehensive evaluation beyond just basic accuracy.
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What we have learnt
- The importance of diverse evaluation metrics for understanding model performance.
- How to construct and interpret a confusion matrix.
- The definitions and applications of accuracy, precision, recall, and F1 score in model evaluation.
- The role of ROC curve and AUC in visualizing model performance across different thresholds.
Key Concepts
- -- Confusion Matrix
- A matrix outlining the performance of a classification model by comparing predicted and actual values, detailing true positives, true negatives, false positives, and false negatives.
- -- Accuracy
- The ratio of correctly predicted observations to the total observations, representing overall correctness of the model.
- -- Precision
- The ratio of true positive predictions to the total predicted positives, indicating the quality of positive predictions.
- -- Recall (Sensitivity)
- The ratio of true positive predictions to the actual positives, measuring the model's ability to capture positive instances.
- -- F1 Score
- The harmonic mean of precision and recall, balancing both metrics to provide a single score that reflects model performance.
- -- ROC Curve
- A curve plotting the true positive rate against the false positive rate to visualize a model's diagnostic ability at various thresholds.
- -- AUC
- The area under the ROC curve, summarizing the overall ability of the model to discriminate between positive and negative classes.
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