There are several evaluation metrics commonly used to assess the performance of multiple regression models. Here are some key evaluation metrics:
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Mean Squared Error (MSE): MSE calculates the average of the squared differences between the predicted and actual values. It measures the average magnitude of the errors, with lower values indicating better model performance.
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Root Mean Squared Error (RMSE): RMSE is the square root of MSE, providing a measure of the average magnitude of the errors in the original scale of the dependent variable. Like MSE, lower RMSE values indicate better model performance.
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R-squared (R²): R-squared represents the proportion of the variance in the dependent variable that can be explained by the independent variables. It ranges from 0 to 1, with higher values indicating a better fit of the model to the data. However, R-squared alone does not indicate the model's predictive accuracy.
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Adjusted R-squared: Adjusted R-squared adjusts for the number of predictors in the model. It penalizes adding irrelevant variables and provides a more reliable measure of the model's goodness of fit. Higher adjusted R-squared values indicate a better trade-off between model complexity and explanatory power.
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Mean Absolute Error (MAE): MAE calculates the average absolute differences between the predicted and actual values. It measures the average magnitude of the errors without considering their direction.
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Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC): AIC and BIC are measures used for model selection. They evaluate the trade-off between model fit and complexity. Lower AIC and BIC values indicate better models.
It's important to note that the choice of evaluation metric depends on the specific context and goals of your regression model. Some metrics may be more suitable for certain scenarios than others. Therefore, it's recommended to consider multiple evaluation metrics and interpret them collectively to gain a comprehensive understanding of the model's performance.
