Model interpretability and understanding for PyTorch
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Updated
Aug 7, 2024 - Python
Model interpretability and understanding for PyTorch
XAI - An eXplainability toolbox for machine learning
Leave One Feature Out Importance
Features selector based on the self selected-algorithm, loss function and validation method
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.
This package can be used for dominance analysis or Shapley Value Regression for finding relative importance of predictors on given dataset. This library can be used for key driver analysis or marginal resource allocation models.
Shapley Interactions for Machine Learning
Using / reproducing DAC from the paper "Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees"
This repository contains the implementation of SimplEx, a method to explain the latent representations of black-box models with the help of a corpus of examples. For more details, please read our NeurIPS 2021 paper: 'Explaining Latent Representations with a Corpus of Examples'.
Predicted and identified the drivers of Singapore HDB resale prices (2015-2019) with 0.96 Rsquare & $20,000 MAE. Web app deployment using Streamlit for user price prediction.
[TNNLS 2022] Significance tests of feature relevance for a black-box learner
This is a custom library for data processing, visualization and machine learning tools.
A Python Package that computes Target Permutation Importances (Null Importances) of a machine learning model.
Toolbox for analysis of model's quality and model's description. For further details see
Implementation of various feature selection methods using TensorFlow library.
This repository contains the implementation of Concept Activation Regions, a new framework to explain deep neural networks with human concepts. For more details, please read our NeurIPS 2022 paper: 'Concept Activation Regions: a Generalized Framework for Concept-Based Explanations.
Implementation of the Integrated Directional Gradients method for Deep Neural Network model explanations.
Feature importance by the permutation method (for fastai V1)
This is the official source code of our IEA/AIE 2021 paper
Predict the outcome of childbirth, from a data set containing socio-economic data of the mother-to-be, and from previous Ante Natal Care checkups
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