Key Concepts in Regression Evaluation
The approach to evaluating machine learning models depends on the problem type. For classification, you predict categories and use metrics like accuracy, precision, recall, and F1 score to compare predicted and true labels. For regression, you predict continuous values, so you use regression metrics to measure how close your predictions are to the actual values and assess model performance.
Evaluating regression models means understanding the errors your model makes. The difference between a prediction and the actual value is a residual. Predictions above the true value are overestimations; below are underestimations. No single metric captures all model weaknesses. Metrics like mean squared error (MSE) highlight large errors, while mean absolute error (MAE) treats all errors equally. Using multiple metrics gives a fuller picture of model performance.
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Can you explain the difference between MSE and MAE in more detail?
What are some other common regression metrics besides MSE and MAE?
How do I choose which regression metric to use for my model?
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The approach to evaluating machine learning models depends on the problem type. For classification, you predict categories and use metrics like accuracy, precision, recall, and F1 score to compare predicted and true labels. For regression, you predict continuous values, so you use regression metrics to measure how close your predictions are to the actual values and assess model performance.
Evaluating regression models means understanding the errors your model makes. The difference between a prediction and the actual value is a residual. Predictions above the true value are overestimations; below are underestimations. No single metric captures all model weaknesses. Metrics like mean squared error (MSE) highlight large errors, while mean absolute error (MAE) treats all errors equally. Using multiple metrics gives a fuller picture of model performance.
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