How To Do Feature Selection In Machine Learning

What is Machine Learning Feature Selection. Regularization This method adds a penalty to different parameters of the machine learning model to avoid over-fitting of the model.

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Like f_regression it can be used in the SelectKBest feature selection strategy and other strategies.

How to do feature selection in machine learning. Feature selection is the key influence factor for building accurate machine learning models. The feature selection can be achieved through various algorithms or methodologies like Decision Trees Linear Regression and Random Forest etc. Lets say for any given dataset the machine learning model learns the mapping between the input features and the target variable.

Import pandas as pd import numpy as np from sklearnfeature_selection import SelectKBest from sklearnfeature_selection import chi2 data pdread_csvDBlogstraincsv X datailoc020 independent columns y datailoc-1 target column ie price range apply SelectKBest class to extract top 10 best features bestfeatures SelectKBestscore_funcchi2 k10. Lets go back to machine learning and coding now. Embedded Methods Implementation.

Here we will do feature selection using Lasso regularization. Some techniques used are. Feature selection is the process of identifying critical or influential variable from the target variable in the existing features set.

Next we select another variable that gives the best performance in combination with the first selected variable. Forward Feature Selection. Hence the features with coefficient.

Comparing to L2 regularization L1 regularization tends to force the parameters of the unimportant features to zero. So for a new dataset where the target is unknown the model can accurately predict the target variable. Popular Feature Selection Methods in Machine Learning.

Feature selection by model Some ML models are designed for the feature selection such as L1-based linear regression and Extremely Randomized Trees Extra-trees model. This approach of feature selection uses Lasso L1 regularization and Elastic nets L1 and L2 regularization. The scikit-learn machine learning library provides an implementation of mutual information for feature selection with numeric input and output variables via the mutual_info_regression function.

This is an iterative method wherein we start with the best performing variable against the target. If the feature is irrelevant lasso penalizes its coefficient and make it 0. This process continues until the preset criterion is achieved.

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