How to do variance thresholding in Python for feature selection

Hits: 67

How to do variance thresholding in Python for feature selection

When working with large datasets, it is often important to select the most important features that contribute to the prediction of a model. One technique for doing this is called variance thresholding.

In Python, variance thresholding can be performed using the library scikit-learn. The first step is to import the library and load the dataset into a pandas dataframe.

Next, create an instance of the VarianceThreshold class and define the threshold for the variance. This threshold determines how much variance a feature must have in order to be selected.

Then, fit the variance threshold to the data and use the get_support() method to identify the selected features. These features can then be used to build a new model with improved accuracy and efficiency.

One important thing to keep in mind when using variance thresholding is that it only considers the variance of the features and does not take into account other important factors like correlation. Therefore, it should be used in combination with other feature selection techniques to get the best results.

In summary, variance thresholding is a simple but effective technique for feature selection in Python. By using the VarianceThreshold class in scikit-learn, it can be easily implemented and help to improve the performance of machine learning models by identifying the most important features.

 

In this Learn through Codes example, you will learn: How to do variance thresholding in Python for feature selection.



 

Personal Career & Learning Guide for Data Analyst, Data Engineer and Data Scientist

Applied Machine Learning & Data Science Projects and Coding Recipes for Beginners

A list of FREE programming examples together with eTutorials & eBooks @ SETScholars

95% Discount on “Projects & Recipes, tutorials, ebooks”

Projects and Coding Recipes, eTutorials and eBooks: The best All-in-One resources for Data Analyst, Data Scientist, Machine Learning Engineer and Software Developer

Topics included: Classification, Clustering, Regression, Forecasting, Algorithms, Data Structures, Data Analytics & Data Science, Deep Learning, Machine Learning, Programming Languages and Software Tools & Packages.
(Discount is valid for limited time only)

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.

Learn by Coding: v-Tutorials on Applied Machine Learning and Data Science for Beginners