Applied Data Science Coding in Python: How to get Feature Importance

Hits: 41

Applied Data Science Coding in Python: How to get Feature Importance

In order to understand the importance of different features in a dataset, you can use a technique called “feature importance.” This technique allows you to see which features in your dataset have the biggest impact on the outcome or target variable.

There are many ways to calculate feature importance in Python, but one popular method is called “Random Forest Feature Importance.” This method uses the Random Forest algorithm, which is an ensemble learning method that creates multiple decision trees and averages their results to make a final prediction.

The idea behind Random Forest Feature Importance is that each decision tree in the forest will have different feature importance scores, so by averaging these scores, you can get a more accurate picture of the overall importance of each feature.

Another popular method is using XGBoost, which is a gradient boosting library that is highly efficient and easy to use. It also has a feature_importance_ attribute which is similar to the random forest feature importance.

It’s worth noting that most of the feature importance methods are based on the concept of permutation importances, which is a way to estimate the importance of a feature by randomly shuffling its values, and then see how much the performance drops.

There are also other feature importance methods like permutation importance, SHAP (SHapley Additive exPlanations) values, and more.

In summary, feature importance is a technique that can help you understand which features in your dataset are most important for the outcome or target variable. There are many ways to calculate feature importance in Python, but two popular methods are Random Forest Feature Importance and XGBoost’s feature_importance_ attribute.

 

In this Applied Machine Learning & Data Science Recipe, the reader will learn: How to get Feature Importance.



Applied Data Science Coding in Python: How to get Feature Importance

 

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

There are 2000+ End-to-End Python & R Notebooks are available to build Professional Portfolio as a Data Scientist and/or Machine Learning Specialist. All Notebooks are only $19.95. We would like to request you to have a look at the website for FREE the end-to-end notebooks, and then decide whether you would like to purchase or not.

Please do not waste your valuable time by watching videos, rather use end-to-end (Python and R) recipes from Professional Data Scientists to practice coding, and land the most demandable jobs in the fields of Predictive analytics & AI (Machine Learning and Data Science).

The objective is to guide the developers & analysts to “Learn how to Code” for Applied AI using end-to-end coding solutions, and unlock the world of opportunities!

https://setscholars.net/machine-learning-for-beginners-in-python-feature-importance/

How to visualise XgBoost model feature importance in Python

How to rank feature with importance in R – Feature selection in R