12. Evaluation Methodologies of AI Models
Evaluating AI models is crucial for understanding their performance in real-world scenarios, including checking predictions, error rates, and ensuring fairness. Various methodologies such as confusion matrices, evaluation metrics, cross-validation, and ROC curves provide frameworks to assess model quality. These techniques not only help in selecting the best-performing models but also address issues of bias and fairness in AI applications.
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What we have learnt
- Evaluation of AI models is essential to determine their accuracy and reliability.
- Metrics such as accuracy, precision, recall, and F1 score quantify model performance.
- Understanding overfitting and underfitting is critical for achieving good generalization in model performance.
Key Concepts
- -- Confusion Matrix
- A table used to evaluate the performance of classification models by comparing actual and predicted values.
- -- Accuracy
- Measures the overall correctness of the model based on the ratio of correctly predicted instances to the total instances.
- -- Precision
- The ratio of true positives to the sum of true and false positives, focusing on how many predicted positives are true.
- -- Recall
- The ratio of true positives to the sum of true positives and false negatives, indicating how many actual positives were captured.
- -- F1 Score
- The harmonic mean of precision and recall, useful for balancing the two when they are in conflict.
- -- CrossValidation
- A technique for assessing how the results of a statistical analysis will generalize to an independent data set.
- -- Overfitting
- A modeling error which occurs when a model is too complex and captures noise instead of the underlying distribution.
- -- ROC Curve
- A graphical plot illustrating the diagnostic ability of a binary classifier system as its discrimination threshold is varied.
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