Which Metrics in Regression matter the most? MSE|RMSE|MAE|R2|Adj R2- Advantages/Disadvantages
Different metrics can be used to evaluate the performance of regression models, and the choice of metric depends on the specific problem and goals of the analysis. Here are some common regression evaluation metrics and their applications:
1 . Mean Squared Error (MSE): This is a commonly used metric that measures the average of the squared differences between the predicted and actual values. It gives more weight to large errors and is sensitive to outliers. MSE is useful when the goal is to minimize the overall error in the model.
2. Root Mean Squared Error (RMSE): This metric is the square root of MSE and has the same interpretation. It is easier to interpret since the units of measurement match the target variable. RMSE is useful when the goal is to minimize the overall error in the model.
3. Mean Absolute Error (MAE): This metric measures the average of the absolute differences between the predicted and actual values. It gives equal weight to all errors and is less sensitive to outliers. MAE is useful when the goal is to minimize the overall error in the model while avoiding large errors.
4. R-squared (R2): This metric measures the proportion of variance in the target variable explained by the model. It ranges from 0 to 1, with higher values indicating better performance. R2 is useful when the goal is to explain the variability in the target variable using the predictors.
5. Adjusted R-squared (Adj R2): This metric is similar to R2 but accounts for the number of predictors used in the model. It penalizes overfitting by adjusting for the number of predictors. Adj R2 is useful when the goal is to explain the variability in the target variable using a parsimonious model.
The choice of metric depends on the specific problem and the goals of the analysis. For example, if the goal is to minimize the overall error in the model, then MSE or RMSE might be more appropriate. If the goal is to explain the variability in the target variable, then R2 or Adj R2 might be more appropriate.
Advantages & disadvantages:
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