jellemzkiválasztás
Jellemzkiválasztás refers to the process of selecting a subset of relevant features from a larger set of observed variables for use in model construction. This selection is crucial because high-dimensional data can lead to computational challenges, overfitting, and reduced model interpretability. The goal is to identify features that have the most predictive power for the target variable while discarding redundant or irrelevant ones.
There are several common approaches to feature selection. Filter methods assess the relevance of features based
The choice of feature selection method depends on the dataset, the machine learning algorithm to be used,