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Discover why you get different R-squared scores when using `cross-validation` in linear regression with Python. Learn best practices for model evaluation!
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Understanding the Discrepancies in R-squared Scores with Cross-Validation

When working with regression models, particularly in Python's scikit-learn, you might notice unexpected variations in your R-squared scores when applying cross-validation. This can be puzzling, especially for those new to the field. In this guide, we'll dive into why these discrepancies arise and how to effectively utilize cross-validation in your modeling process.

The Problem: Different R-squared Scores

Recently, I encountered a situation where I was training a regression model and obtained an R-squared score of around 0.5. However, when utilizing cross-validation with different data subsets, the R-squared values varied significantly. Here’s how I experienced it:

Model Initialization: I started by loading the Boston housing dataset and training a LinearRegression model.

Applying Cross-Validation: Upon using the cross-validation function on different datasets derived from training and testing splits, the scores displayed a vast range of outcomes. For instance, when using the training dataset, the R-squared values were quite different from those when using the test dataset.

Understanding Cross-Validation

Before proceeding, let’s clarify what cross-validation is and its importance:

Purpose: Cross-validation is a technique used to assess the generalizability of a model by dividing the data into subsets. It helps in understanding how well a model performs on unseen data.

Process: The typical process is as follows:

Split your dataset into training and testing sets.

Train your model using cross-validation to evaluate its performance stability.

Finally, assess its score on the test set, which has not been used for training.

How cross_val_score Works

The cross_val_score function in scikit-learn plays a crucial role in this process. It trains the model multiple times on different sections of the dataset and returns a list of accuracy scores. Here's how it functions:

Training: It trains the model using different portions of the training dataset each time.

Scoring: After training, it calculates the score (e.g., R-squared) for each fold, giving you insight into the model’s consistency.

Best Practices for Using Cross-Validation

To properly use cross-validation and ensure you’re interpreting your results correctly, follow these best practices:

1. Use Training Data for Cross-Validation

Always perform cross-validation on the training set only. For example, you can implement it as follows:

[[See Video to Reveal this Text or Code Snippet]]

This ensures that you are evaluating the model on a dataset that it is meant to learn from without introducing test set data at this stage.

2. Assess Mean and Variability of Scores

When you receive the output of R-squared scores from cross-validation, assess both the average score and its variability across different folds. A stable model will show similar R-squared values across different folds.

3. Use Test Set for Final Evaluation

Reserve your test set for the final evaluation of the model. This will give you a true understanding of how the model performs on unseen data after tuning and training.

Conclusion

In conclusion, differences in R-squared scores observed during cross-validation can primarily be attributed to the portions of data being used. By adhering to best practices—such as using the training dataset for cross-validation and keeping your test set for final assessments—you'll have a clearer understanding of your model's performance.

By staying organized and methodical in your approach, you can unlock the power of scikit-learn while enhancing the reliability of your regression models.

If you have any questions or need deeper insights int