What are the key metrics for evaluating regression models?
An example of an AI chatbot conversation about metrics for regression model evaluation.
What are the key metrics for evaluating regression models?
The key metrics for evaluating regression models include R-squared, Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE).
Can you explain what R-squared means?
R-squared measures the proportion of variance in the dependent variable that can be explained by the independent variables. A higher R-squared indicates a better fit.
How do I interpret Mean Absolute Error?
Mean Absolute Error (MAE) represents the average absolute difference between predicted and actual values. Lower MAE indicates better model performance.
Are there any limitations to these metrics?
Yes, each metric has limitations. For example, R-squared can be misleading in non-linear models, and MAE does not penalize larger errors as much as MSE.
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