Contents
Why do we need to use feature selection methods?
Models have increasing risk of overfitting with increasing number of features. Feature Selection methods helps with these problems by reducing the dimensions without much loss of the total information. It also helps to make sense of the features and its importance.
How are feature importance calculated in tree based models?
The feature importance in tree based models are calculated based on Gini Index, Entropy or Chi-Square value. Feature Selection as most things in Data Science is highly context and data dependent and there is no one stop solution for Feature Selection. The best way to go forward is to understand the mechanism of each methods and use when required.
Which is better sklearn or F-test for feature selection?
Advantage of using mutual information over F-Test is, it does well with the non-linear relationship between feature and target variable. Sklearn offers feature selection with Mutual Information for regression and classification tasks. F-Test captures the linear relationship well.
When to use mutual information in feature selection?
Mutual Information captures any kind of relationship between two variables. http://scikit-learn.org/stable/auto_examples/feature_selection/plot_f_test_vs_mi.html This method removes features with variation below a certain cutoff. The idea is when a feature doesn’t vary much within itself, it generally has very little predictive power.
Which is feature selection method ignores the target variable?
Unsupervised feature selection techniques ignores the target variable, such as methods that remove redundant variables using correlation. Supervised feature selection techniques use the target variable, such as methods that remove irrelevant variables..
Which is an input variable in feature selection?
Input variables are those that are provided as input to a model. In feature selection, it is this group of variables that we wish to reduce in size. Output variables are those for which a model is intended to predict, often called the response variable.
How are statistical measures used in feature selection?
The statistical measures used in filter-based feature selection are generally calculated one input variable at a time with the target variable. As such, they are referred to as univariate statistical measures. This may mean that any interaction between input variables is not considered in the filtering process.
What is the definition of feature engineering and selection?
Feature engineering and selection are the methods used for achieving this goal. In this context, the definition of a feature will be a column or attribute of the data. Feature engineering is a broad term that covers a number of manipulations that may be carried out on your dataset.
Why is it important to select the correct features for a model?
Engineering and selecting the correct features for a model will not only significantly improve its predictive power, but will also offer the flexibility to use less complex models that are faster to run and more easily understood.
Why do we use feature selection in PCA?
Feature selection is applied either to prevent redundancy and/or irrelevancy existing in the features or just to get a limited number of features to prevent from overfitting. Note that if features are equally relevant, we could perform PCA technique to reduce the dimensionality and eliminate redundancy if that was the case.