Machine Learning Tutorial Python - 17: L1 and L2 Regularization | Lasso, Ridge Regression
In this Python machine learning tutorial for beginners, we will look into,
1) What is overfitting, underfitting
2) How to address overfitting using L1 and L2 regularization
3) Write code in Python and sklearn for housing price prediction where we will see a model overfit when we use simple linear regression. Then we will use Lasso regression (L1 regularization) and ridge regression (L2 regression) to address this overfitting issue
Code: https://github.com/codebasics/py/tree/master/ML/16_regularization
#MachineLearning #PythonMachineLearning #MachineLearningTutorial #Python #PythonTutorial #P…
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⚡
AI Lesson Summary
✦ V3 skills
🛠 Hands-on
This video tutorial teaches how to use L1 and L2 regularization techniques to address overfitting in machine learning, specifically focusing on Lasso and Ridge regression for housing price prediction. By the end of this tutorial, viewers will be able to build a simple linear regression model, implement regularization techniques, and improve model accuracy.
Key Takeaways
- Import the necessary libraries and load the dataset
- Create a simple linear regression model
- Implement L1 and L2 regularization using Lasso and Ridge regression
- Evaluate the model's performance using metrics such as accuracy and R-squared
- Tune hyperparameters to improve model accuracy
💡 Regularization techniques such as L1 and L2 regularization can significantly improve the accuracy of machine learning models by reducing overfitting and improving generalization.
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