Evaluating linear regression models is a critical step in understanding how well your model fits your data. In this thread, we’ll explore common mistakes people make when evaluating linear regression models and provide a correct code snippet for each mistake.
1. Focusing only on R-squared score:
One common mistake when evaluating linear regression models is focusing solely on the R-squared (R2) score as the primary evaluation metric. While R2 tells how well the model explains the variance in the data, it may not provide a complete picture of model performance. The code below uses different metrics to evaluate the model’s performance and not only focuses on the R2 score:
2. Not performing cross-validation:
Cross-validation is a crucial step in evaluating the performance of a model, including linear regression. It involves splitting the dataset into multiple subsets (folds) and systematically training and testing the model on different combinations of these subsets. The primary goal is to assess how well the model generalizes to unseen data. The code below performs cross-validation and then evaluates the results:
3. Not calculating and analyzing residuals:
Another common mistake people make is not calculating and thoroughly analyzing the residuals. Residuals represent the differences between the actual and predicted values, and they can provide valuable insights into the model’s performance and any potential issues. Here is how you can calculate the residuals:
- Residuals can be analyzed by checking for linearity, homoscedasticity, and normality through visual inspection of plots like scatter plots of residuals vs. predicted values, residual histograms, and Q-Q plots.
- Additionally, statistical tests and summary statistics can help assess residuals’ properties.
- In the code, we’ve only calculated residuals, and further analysis can be performed using various statistical methods and visualization techniques.