explain PCA and feature selection technique ?

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Accepted Answer

PCA is a dimensionality reduction technique that transforms data into a lower-dimensional space. Feature selection is the process of selecting a subset of relevant features for use in model training.

PCA helps in reducing the dimensionality of data by finding the principal components that explain the most variance in the data.
Feature selection involves selecting the most important features from the dataset based on certain criteria like correlation, importance scores, or domain knowledge.
PCA can be used to reduce the number of features before applying feature selection techniques to further refine the feature set.
Examples of feature selection techniques include Recursive Feature Elimination (RFE), Lasso regression, and tree-based feature selection methods.

explain PCA and feature selection technique ?

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