Taming Overfitting with Ridge Regression: A Python Insights Story

Today's data exploration journey took me through the heart of linear regression, where I uncovered the power of regularization in combating overfitting.

Key takeaways:

• Overfitting Alert: My initial linear regression model performed well on training data but faltered on unseen test data, a classic sign of overfitting.
• Ridge to the Rescue: By introducing L2 regularization with Ridge regression, I effectively penalized large coefficients, encouraging model simplicity and generalization.
• The Results Are In : Ridge regression achieved a higher test set accuracy than linear regression, demonstrating its ability to generalize better to new data.
• Lesson Learned In the tradeoff between training performance and generalization, Ridge regression often emerges as the wiser choice for building robust predictive models for small datasets

Below is the link if you would like to see the Python code behind this analysis.

https://github.com/Shoaib-Zafer/Introduction-to-Machine-Learning-Using-Python/blob/master/1.3%20Linear-Ridge%20Regression%20on%20Boston%20Housing%20Dataset.py

Share your own experiences with working with the Boston Housing dataset in Comments