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Evaluating Performance
Evaluating Performance
After training a machine learning model, its performance must be assessed to determine its effectiveness. For different types of models, evaluation methods vary:
Regression Models: Metrics such as mean squared error (MSE) and other average loss functions measure performance.
Classification Models: Performance is typically evaluated using a confusion matrix, precision, recall, and accuracy metrics.
Confusion Matrix
Confusion Matrix
A confusion matrix summarizes the prediction results for classification tasks. It compares predicted classifications against actual classifications and includes:
True Positives (TP): Correctly predicted positive cases.
True Negatives (TN): Correctly predicted negative cases.
False Positives (FP): Incorrectly predicted positive cases.
False Negatives (FN): Incorrectly predicted negative cases.
From the confusion matrix, additional metrics can be calculated:
Precision: The ratio of correct positive predictions to total positive predictions.
Precision = TP / (TP + FP)
Recall: The ratio of correct positive predictions to total actual positive cases.
Recall = TP / (TP + FN)
Accuracy: The ratio of all correct predictions to total cases.
Accuracy = (TP + TN) / (TP + FP + FN + TN)
Trade-offs Between Metrics
Trade-offs Between Metrics
Precision and recall often involve trade-offs depending on the application:
High Precision: Prioritized when false positives are costly (e.g., diagnosing diseases).
High Recall: Prioritized when missing true positives is critical (e.g., fraud detection).
These evaluation techniques allow data analysts to identify areas for model improvement and balance metrics according to specific use-case requirements.
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