permutation-importance

A Python Package that computes Target Permutation Importances (Null Importances) of a machine learning model.

This project aims to study the influence factors of international students' mobility with the case of international students from B&R countries studying in China.

eXplainable Artificial Intelligence (XAI) Basic Algorithms on Iris Dataset

🧠 EEG preprocessing and classification pipeline for ASD research using MUSE EEG data

High data dimensionality and irrelevant features can negatively impact the performance of machine learning algorithms. This repository implements the Permutation feature importance method to enhance the performance of some machine learning models by identifying the contribution of each feature used.

These training sessions in machine learning, conducted by Yandex, are dedicated to classical machine learning. This offers an opportunity to reinforce theoretical knowledge through practice on training tasks.

Contains analysis of Lyft ride attributes and how it affects demand surge in the city of Boston.

This project fits and tunes several regression models to predict Parkinson's symptom severity scores from voice recordings.

This project contains the data, code and results used in the paper title "On the relationship of novelty and value in digitalization patents: A machine learning approach".

A solid foundational understanding of XAI, primarily emphasizing how XAI methodologies can expose latent biases in datasets and reveal valuable insights.

A comprehensive resource for understanding, implementing, and comparing various methods for feature importance in machine learning. This repository includes theoretical explanations, practical examples, and code snippets for techniques like permutation importance, SHAP, LIME, and more.

This repo is all about feature importance. Whereby we look at the ways one can identify if a feature is worth having in the model or rather if it has a significant influence in the prediction. The methods are model-agnostic.

Machine learning approach for feature selection and uncertainty analysis in wastewater treatment plant biogas production. Explores advanced ML techniques for optimizing renewable energy processes.

Hybrid financial forecasting system integrating technical analysis with FinBERT sentiment scores, using LSTM/CNN models and XAI (SHAP/LIME) for interpretability.

A take on highly imbalanced fraud classification using permutation importance to select top features and explaining the model using SHAP.

How far does a conclusion about educational attainment move across modelling choices that are all individually defensible? A 48-specification sensitivity study of PISA 2022 UK mathematics outcomes.

This project evaluates whether the wisdom of the crowd improves wage prediction accuracy. Multiple regression models—Linear, Ridge, KNN, Decision Tree, and SVR—are compared against a tuned VotingRegressor ensemble to determine if the ensemble outperforms individual models.

Semiconductor yield prediction on UCI SECOM: missing-value strategy comparison, LightGBM vs Keras DNN on imbalanced data, permutation importance for key sensor stations

Which data-quality defect breaks which trust metric? A reproducible corruption sweep over accuracy, calibration and attribution stability - with serving-time and training-time corruption modelled separately

Machine Learning pipeline for smartphone price prediction using exploratory data analysis, feature engineering, model comparison, hyperparameter tuning, and XGBoost optimization.