High R2 value with noise

AI Discussions
1

As a result of ML model, i got high R2 value equals 0.99 but when I plot scatters it seems not perfect i.e. there is noise in the scatter plot
what does that mean?

image

2

any body has clarification

3

Hi @IZZETTIN_ALHALIL

Thank you for your post.

Having a high R-squared (R2) value of 0.99 indicates that your machine learning model explains a large proportion of the variance in the dependent variable. It means that approximately 99% of the variability in the target variable (the variable you are trying to predict) can be accounted for by the model. This is generally a good sign and suggests that your model is fitting the data well.

However, the presence of noise in the scatter plot despite a high R2 value could indicate a few things:

  1. Overfitting: Overfitting occurs when the model fits the training data extremely well but fails to generalize to new, unseen data. It may capture the noise and outliers in the training data, leading to an overly complex model that does not perform well on new data.

  2. Outliers: Outliers are data points that deviate significantly from the rest of the data. High R2 values are sensitive to outliers, and they can have a substantial impact on the model’s performance.

  3. Underlying Complexity: The underlying relationship between the features and the target variable might be more complex than what the model is capturing. As a result, the model may not perfectly fit the data points, even with a high R2 value.

  4. Measurement Errors: In some cases, the presence of measurement errors or uncertainties in the data can lead to noise in the scatter plot, even if the model is performing well.

To address these issues and improve the model’s performance:

  • Check for Overfitting: Evaluate your model on a separate validation or test dataset to see if it generalizes well beyond the training data. If there’s a significant drop in performance on the validation set, it suggests overfitting.

  • Deal with Outliers: Identify and handle outliers appropriately. Depending on the context, you may choose to remove outliers or use robust regression techniques that are less sensitive to outliers.

  • Feature Engineering: Consider whether additional features or transformations of existing features might improve the model’s performance.

  • Regularization: If overfitting is a concern, consider using regularization techniques such as L1 (Lasso) or L2 (Ridge) regularization, which can help control model complexity.

  • Model Selection: Experiment with different machine learning algorithms and model architectures to find the best model for your data.

  • Data Quality: Ensure the quality of your data by cleaning, preprocessing, and validating it thoroughly.

Keep in mind that while R2 is a useful metric, it is essential to look at other evaluation metrics and visualize the model’s performance on both training and test data to get a comprehensive understanding of its capabilities. Additionally, understanding the domain and the data context is crucial for interpreting and improving the model’s results.

Here it is a good article about R2 and some pitfalls at using it

I hope this helps

Best regards
elirod