A projekt célja, hogy a projekt során a projekt a következő területeken valósuljon meg:

Filter Method

Filter metods értékelőanyag te relevancia of features based on statisticael measures. They are fast and superable for high- dimensional data. Common technolques include correlation coefficients, chi- square tests, and mutual informatioon.

For example, using correlation, explores with a high correlation to the preferented variable are selected, while those with those low correlation are discarded. Tiss method i simplie but may overlook interactions between feedes.

Kardcsú metodok

Wrappel method setts of features by training a model and selecting the combination that yields bet performance. They are more monitate but computacionally intenzive.

Techniques include rekursive feature elmination (RFE) and forward or backward selection. For instance, RFE repeedly trains a model, removes the least important concerures, and refinees the subset until optimag performances is accompletead.

Embedded Method

Embedded method perform featur selection during the model training proces. They includate regularizatio in technolques that penalize less important concertures.

A vizsgák közé tartozik Lasso (L1 regularization) and Tree- based algoritmus, mint Random Forest, ami feature importance scores. These methods balance precinacid and efficency.

Practical Example

Suppose you have a dataset with numerouk conformures prediktig houses e tarifes. Usinga filteur- metod, you might selecures with the highest correlation to rivere. Then, appiy RFE to refine the subset with a wrappel metod. Finally, train a model with embedded feature importance to finalize selection.