permutation-importance
There are 22 repositories under permutation-importance topic.
kingychiu/target-permutation-importances
A Python Package that computes Target Permutation Importances (Null Importances) of a machine learning model.
peijin0405/Machine-Learning-Analysis-of-International-Student-Mobility
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.
SeyedMuhammadHosseinMousavi/eXplainableAI-XAI-Basics-Python
eXplainable Artificial Intelligence (XAI) Basic Algorithms on Iris Dataset
andrewlee977/lyft-demand-surge
Contains analysis of Lyft ride attributes and how it affects demand surge in the city of Boston.
BobbyWilt/PD_Voice_UPDRS
This project fits and tunes several regression models to predict Parkinson's symptom severity scores from voice recordings.
Harshith8333/EEG-Task-Classification-ASD
š§ EEG preprocessing and classification pipeline for ASD research using MUSE EEG data
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.
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.
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".
sandiindika/bin-FQL
Klasifikasi Musik Berdasarkan Genre Menggunakan Metode Naive Bayes.
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.
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.
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.
duygut/hotel_booking_cancelation_with_tree_based_algorithms
Comparing different tree-based algorithms to find the best model for cancelation prediction
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.
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.
wangyuhsin/feature-importance-and-selection
Feature importance refers to a measure of how important each feature/variable is in a dataset to the target variable or the model performance. It can be used to understand the relationships between variables and can also be used for feature selection to optimize the performance of machine learning models.
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.
ILaskira/Soaring-Stock-Prediction-Challenge
Competition project for classifying soaring stocks using XGBoost and stacking-based ensemble with advanced feature selection and threshold tuning.
ILaskira/Stock-Price-Prediction
Stock price forecasting using LSTM, FFT denoising, and GARCH confidence intervals on UMC (2303.TW).
sarikayamehmet/Fraud-Classification
A take on highly imbalanced fraud classification using permutation importance to select top features and explaining the model using SHAP.