Model interpretability and understanding for PyTorch
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Updated
May 31, 2024 - Python
Model interpretability and understanding for PyTorch
SHAP Interaction Quantification (short SHAP-IQ) is an XAI framework extending on the well-known shap explanations by introducing interactions i.e. synergy scores.
Weighted Shapley Values and Weighted Confidence Intervals for Multiple Machine Learning Models and Stacked Ensembles
⛳️ This project, within the course Sports Analytics, TDDE64, at Linköping University, uses Random Forest and SVM models to predict tournament outcomes, revealing insights into the factors that drive player success in golf.
Customer Attrition Prediction with Python
Beta Machine Learning Toolkit
Prediction of students' dropout using classification models. Data visualisation, feature selection, dimensionality reduction, model selection and interpretation, parameters tuning.
Malicious URL detector built with deep exploration on feature engineering.
ProphitBet is a Machine Learning Soccer Bet prediction application. It analyzes the form of teams, computes match statistics and predicts the outcomes of a match using Advanced Machine Learning (ML) methods. The supported algorithms in this application are Neural Networks, Random Forests & Ensembl Models.
A Julia package for interpretable machine learning with stochastic Shapley values
The repository contains comprehensive assessment reports and Jupyter Notebook files aimed at addressing key questions related to predicting wireless churn and identifying the features driving churn.
Content: Root node, Decision node & Leaf nodes, Attribute Selection Measure (ASM), Feature Importance (Information Gain), Gini index
ML modeling and feature importance analysis conducted to identify/inform company practices related work interference due to mental health.
Jupyter notebook using machine learning techniques to explore the complex drivers of modern slavery. Models from a research paper are replicated and evaluated . Actions also include filling missing data, training regression models, and analyzing feature importance.
Analyzing Fater company's diaper market potential and enhancing revenue estimation for Naples stores: A Socio-Demographic, Territorial, and Points of Interest Perspective
[TNNLS 2022] Significance tests of feature relevance for a black-box learner
Sector based classification with feature engineering and tsfresh. Looking 3 months momentum of stocks.
House-Price-Prediction-App
A Python Package that computes Target Permutation Importances (Null Importances) of a machine learning model.
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