Machine Learning Tutorial Python - 17: L1 and L2 Regularization | Lasso, Ridge Regression

Summary

The video discusses the problem of overfitting in machine learning and introduces L1 and L2 regularization techniques to address it. Using a housing price dataset from Melbourne, the video demonstrates how a simple linear regression model tends to overfit, and how applying L1 (LASSO) and L2 (ridge) regularization improves accuracy on unseen data. The tutorial explains key concepts, compares underfitting, overfitting, and balanced fitting, and walks through the code for implementing both regularization methods to enhance model performance.

Takeaways

• 📉 Overfitting is a common problem in machine learning, and L1 and L2 regularization techniques are used to address it.
• 📊 In this tutorial, the Melbourne housing price dataset is used to demonstrate how regularization improves model performance.
• 🧮 Overfitting occurs when the model fits the training data too closely, leading to poor generalization on unseen test data.
• 📈 L1 regularization (LASSO) and L2 regularization (Ridge) help prevent overfitting by penalizing large coefficients in the model.
• 📏 Linear regression without regularization results in overfitting, with the model performing well on training data but poorly on test data.
• 🔧 L1 regularization uses the absolute value of the coefficients, shrinking them to zero, which simplifies the model.
• 🧲 L2 regularization uses the squared value of the coefficients, shrinking them but keeping all variables in the model.
• ⚙️ L1 regularization is implemented using Sklearn's LASSO, and L2 regularization is implemented with Ridge regression.
• 🚀 Regularization significantly improved the model's performance, raising test accuracy from 14% to around 67%.
• 📚 The video provides a step-by-step guide to implementing these techniques in Python, and emphasizes the importance of balancing between underfitting and overfitting.

Q & A

What is overfitting in machine learning?

Overfitting occurs when a model learns the details and noise in the training data to the extent that it negatively impacts the performance of the model on new data. The model fits too closely to the specific training data, making it less effective in predicting outcomes for unseen data.

What techniques are discussed to address overfitting in the video?

The video discusses using L1 and L2 regularization techniques (LASSO and Ridge regression) to address overfitting. These techniques add a penalty term to the loss function to shrink the model parameters, thereby preventing the model from becoming overly complex and fitting too closely to the training data.

What is L1 regularization?

L1 regularization, also known as LASSO regression, adds the absolute values of the coefficients as a penalty term to the loss function. This technique encourages sparsity, meaning it drives some coefficients to zero, effectively removing irrelevant features from the model.

What is L2 regularization?

L2 regularization, also known as Ridge regression, adds the square of the coefficients as a penalty term to the loss function. This technique shrinks the coefficients, but unlike L1, it generally keeps all features rather than driving some coefficients to zero.

Why is regularization important for machine learning models?

Regularization helps prevent overfitting by adding a penalty term to the loss function, which constrains or regularizes the coefficients of the model. This ensures that the model doesn't become too complex and remains general enough to perform well on unseen data.

What dataset is used in the video for demonstrating regularization?

The video uses a housing price dataset from Melbourne, which includes features like the number of rooms, distance to the city center, and postal codes, among others, to build and demonstrate the models.

How does the video illustrate the concept of overfitting using a linear regression model?

The video demonstrates building a simple linear regression model on the Melbourne housing dataset. The model performs well on the training data but poorly on the test data, indicating overfitting as the model cannot generalize to unseen data.

What happens when the linear regression model is evaluated without regularization?

When evaluated without regularization, the model achieves high accuracy on the training set but very low accuracy (around 14%) on the test set, clearly showing that the model is overfitting to the training data and not generalizing well.

How does LASSO (L1) regularization improve model performance?

By applying LASSO regularization, the video shows that the test accuracy improves significantly, as the L1 penalty reduces the influence of less important features. The model becomes simpler and more generalized, boosting the test score to around 67%.

What role does the parameter ‘alpha’ play in LASSO and Ridge regression?

The parameter ‘alpha’ controls the strength of the regularization. A higher alpha value increases the regularization effect, shrinking the coefficients more, while a lower alpha value reduces the effect, allowing larger coefficients.