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1. Filter Method
2. Wrapper Method
3. Embedded Method
4. Dimensionality Reduction
5. Regularization Method
6. Deep Learning based Method
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These all are used to make the model's performance better!
1. Filter method: uses basic statistic methods to find the best features.
2. Wrapper Method: Uses Machine Learning Algorithms to select the best features. [Forward Selection, Backward Elimination, Recursive Feature Elimination, Exhaustive Feature Selection] 3. Embedded Method: Uses a combination of both Filter and Wrapper methods 4. Dimensionality Reduction: [PCA, SVD, t-SNE, LDA (Linear Discriminative Analysis] 5. Regularization Method: Lasso and Ridge Regression (combination of both makes Elastic Net Regularization) 6. Deep Learning based Method: Auto Encoders
Done
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Learned about feature selection methods like filter methods, wrapper methods, embedded methods, dimensionality reduction methods, regularization methods, deep learning-based methods
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Lecture done
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