?? Day 137 of 365: Embedded Methods ??

Hey, Embeddors!

Welcome to Day 137 of our #365DaysOfDataScience journey! ??

Today we’re wrapping up our exploration of feature selection with embedded methods. These methods combine feature selection with model training, automatically identifying the most important features while the model is learning. Let's dive into some powerful techniques!

?? What We’ll Be Exploring Today:

- Embedded Methods for Feature Selection:

??- These methods work by incorporating feature selection directly into the model training process, using techniques like regularization or feature importance scores.

- Key Techniques:

??1. Lasso (L1) Regularization: Encourages sparse models by shrinking some feature coefficients to zero, effectively selecting features.

??2. Ridge (L2) Regularization: Penalizes large coefficients to prevent overfitting, though it doesn’t shrink them to zero like Lasso.

??3. Decision Tree-Based Feature Importance: Models like Random Forest provide feature importance scores based on how much each feature contributes to reducing uncertainty in the splits.

- Why Use Embedded Methods?

??- They’re efficient because they perform feature selection and model training simultaneously, saving time while improving generalization.

?? Learning Resources:

- Read: Check out articles on Lasso and Ridge regularization, and review the Scikit-learn documentation on [Feature Importance](https://scikit-learn.org/stable/modules/feature_selection.html#embedded-methods).

- Watch: This YouTube video "Lasso, Ridge, and ElasticNet in Feature Selection" is perfect for today’s lesson!

?? Today’s Task:

1. Apply Lasso (L1 regularization) on a dataset to automatically select important features.

2. Compare the selected features and model performance with the RFE results from Day 136.

3. Analyze the differences in feature selection and how they impact the model's accuracy or interpretability.

I’m excited to see how Lasso selects features and how it stacks up against RFE! Let’s embed this new technique into our workflow and uncover the best features together ??.

Enjoy experimenting with embedded methods today! We’re almost pros at feature selection now!

Happy Learning & See You Soon!

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