Home Artificial Intelligence A Guide to 21 Feature Importance Methods and Packages in Machine Learning (with Code)

A Guide to 21 Feature Importance Methods and Packages in Machine Learning (with Code)

A Guide to 21 Feature Importance Methods and Packages in Machine Learning (with Code)

From the OmniXAI, Shapash, and Dalex interpretability packages to the Boruta, Relief, and Random Forest feature selection algorithms

Towards Data Science
Image created by the creator at DALL-E

“We’re our decisions.” —Jean-Paul Sartre

We live within the era of artificial intelligence, mostly due to incredible advancement of Large Language Models (LLMs). As vital because it is for an ML engineer to study these recent technologies, equally vital is his/her ability to master the basic concepts of model selection, optimization, and deployment. Something else could be very vital: the input to the above, which consists of the data features. Data, like people, have characteristics called features. Within the case of individuals, you need to understand their unique characteristics to bring out the very best in them. Well, the identical principle applies to data. Specifically, this text is about feature importance, which measures the contribution of a feature to the predictive ability of a model. We’ve got to know feature importance for a lot of essential reasons:

  • Time: Having too many features slows down the training model time and likewise model deployment. The latter is especially vital in edge applications (mobile, sensors, medical diagnostics).
  • Overfitting. If our features aren’t rigorously chosen, we’d make our model overfit, i.e., study noise, too.
  • Curse of dimensionality. Many features mean many dimensions, and that makes data evaluation exponentially tougher. For instance, k-NN classification, a widely used algorithm, is greatly affected by dimension increase.
  • Adaptability and transfer learning. That is my favorite reason and actually the explanation for writing this text. In transfer learning, a model trained in a single task will be utilized in a second task with some finetuning. Having a great understanding of your features in the primary and second tasks can greatly reduce the fine-tuning you want to do.

We’ll give attention to tabular data and discuss twenty-one ways to evaluate feature importance. One might wonder: ‘Why twenty-one techniques? Isn’t one enough?’ It is crucial to…


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