Learn to automate feature selection with lasso regression
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Automate feature selection in your machine learning models using Lasso Regression. Learn how to use Lasso for both regression and feature selection to create more efficient and accurate predictive models by eliminating irrelevant features.
At a Glance
Learn feature automation with lasso regression using sklearn in Python. Optimize model performance by using regularization techniques and hyperparameter tuning with different Python libraries. Explore why this technique is crucial for feature selection through the creation of insightful data visualizations, while you gain practical experience with lasso regression, a powerful tool for optimizing models and elevating predictive performance.
This hands-on project is based on the Apply lasso regression to automate feature selection tutorial. The Guided Project format combines the instructions of the tutorial with the environment to execute these instructions without the need to download, install, and configure tools.
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A look at the project ahead
- Gain a solid understanding of regularization concepts in the context of linear regression models.
- Learn to load and manipulate data sets using essential libraries such as NumPy and Pandas.
- Implement lasso regression for linear models using sklearn, and use grid search for hyperparameter tuning.
What you’ll need
- No installation required: Everything is available in the JupyterLab, including any Python libraries and data sets.
- Basic understanding of Python: This project is beginner-friendly, but having a basic understanding of Python will make it easier.
- Basic understanding of statistical concepts: A basic understanding of statistic concepts is beneficial but not required. This tutorial begins with an explanation of lasso regression to guide you throughout the project.
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