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Feature Permutation Importance

Here you’ll find all articles related to feature permutation importance, whether its Python tutorials or conceptual articles.

Feature permutation importance is a method for evaluating the importance of individual features in a machine learning model. It works by randomly permuting the values of a single feature and then measuring the impact on the model’s performance. The idea is that if a feature is important, then randomly permuting its values should significantly degrade the model’s performance, whereas permuting an unimportant feature should have little or no effect. The degree of degradation can be quantified and used to rank the features in order of importance. Feature permutation importance is a model-agnostic method. As such, it can be applied to any machine learning model, regardless of its type or underlying algorithms.  It is particularly useful for understanding which features are driving a model’s predictions and for identifying and removing unimportant or redundant features from a dataset.

Feature Engineering and Selection for Regression Models with Python and Scikit-learn

March 6, 2023September 26, 2022
car price prediction machine learning tutorial python-min

Training a machine learning model is like baking a cake: the quality of the end result depends on the ingredients … Read more

Customer Churn Prediction – Understanding Models with Feature Permutation Importance using Python

December 27, 2022August 2, 2020
churn prediction python

One of the primary goals of many service companies is to build solid and long-lasting relationships with their customers. Customers … Read more

Classifying Purchase Intention of Online Shoppers with Python

March 2, 2023May 11, 2020
Customer Purchase Intention Prediction Python Machine Learning

Most online stores welcome countless visitors every day, but only a fraction of those visitors will make a purchase. Purchase … Read more

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