In machine learning, more data isn’t always better. That’s where feature selection comes in—helping you pick only the most relevant variables to improve model accuracy, reduce overfitting, and speed up training.

There are three main types of feature selection techniques:

• Filter methods (e.g., Information Gain, Chi-Square) use statistical scores to rank features.
• Wrapper methods (like Forward/Backward Selection) evaluate subsets using actual models.
• Embedded methods (like LASSO, Random Forest) perform selection during training.

For unlabeled data, unsupervised techniques like PCA, ICA, and NMF help find structure and reduce dimensionality.

Choosing the right features not only boosts performance—it makes your models easier to interpret and deploy. Whether you're working on image classification or predictive modeling, mastering feature selection is a must-have skill.

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