HMesghali/Biogas-Production-Machine-Learning-Analysis

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

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AbbasPak/Feature-Importance-in-Machine-Learning

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.

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kingychiu/target-permutation-importances

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

Language: Python - Size: 693 KB - Last synced at: 3 days ago - Pushed at: 4 months ago - Stars: 14 - Forks: 1

HROlive/Introduction-to-Explainable-Deep-Learning-on-Supercomputers

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

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Kritika97Gaikwad/AI-Generated-Text-Detection

Developed a machine learning model using scikit-learn, implementing ensemble techniques, PCA, correlation analysis, and extensive feature engineering. The goal was to classify documents as either human-generated (0) or AI-generated (1) based on document embeddings, word count, and punctuation.

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SirWilliam254/Feature-Importance

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.

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janasatvika/Optimizing-Classification-Models-using-Permutation-Feature-Importance-Method

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.

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peijin0405/Machine-Learning-Analysis-of-International-Student-Mobility-A-Case-Study-of-China

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.

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SeyedMuhammadHosseinMousavi/eXplainableAI-XAI-Basics-Python

eXplainable Artificial Intelligence (XAI) Basic Algorithms on Iris Dataset

Language: Python - Size: 396 KB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 1 - Forks: 0

Tikhon-Radkevich/CourseYandexML

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.

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vikram-raju/Permutation-Importance-and-SHAP-on-Fraud-Classification

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

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MichaelAlexanderBryant/concrete-strength-prediction

An end-to-end project to analyze and model concrete compressive strength data then productionize the best model to help civil engineers determine concrete structural integrity.

Language: Python - Size: 5.76 MB - Last synced at: about 2 years ago - Pushed at: about 3 years ago - Stars: 1 - Forks: 0

nilsdenter/novelty_value_ml

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".

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BobbyWilt/PD_Voice_UPDRS

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

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GP-TEAM-SBME/10-Year-Risk-of-Death-of-Individuals-from-the-NHANES-I-Epidemiology-Dataset

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andrewlee977/lyft-demand-surge

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

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sarikayamehmet/Fraud-Classification Fork of vikram-raju/Permutation-Importance-and-SHAP-on-Fraud-Classification

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

Language: Jupyter Notebook - Size: 1.62 MB - Last synced at: about 2 years ago - Pushed at: almost 6 years ago - Stars: 0 - Forks: 0