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17 search results found

Captum ⭐ 4,393

Model interpretability and understanding for PyTorch

Lofo Importance ⭐ 785

Leave One Feature Out Importance

Feature Selection ⭐ 475

Features selector based on the self selected-algorithm, loss function and validation method

Prophitbet Soccer Bets Predictor ⭐ 192

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.

Dominance Analysis ⭐ 128

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.

Hierarchical Dnn Interpretations ⭐ 119

Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)

Deep Explanation Penalization ⭐ 74

Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" https://arxiv.org/abs/1909.13584

Isarn Sketches Spark ⭐ 27

Routines and data structures for using isarn-sketches idiomatically in Apache Spark

Disentangled Attribution Curves ⭐ 23

Using / reproducing DAC from the paper "Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees"

Hdb_resale_prices ⭐ 18

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.

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'.

Dnn Inference ⭐ 13

[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.

Target Permutation Importances ⭐ 11

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

Pytolemaic ⭐ 10

Toolbox for analysis of model's quality and model's description. For further details see

SHAP Interaction Quantification (short SHAP-IQ) is an XAI framework extending on the well-known shap explanations by introducing interactions i.e. synergy scores.

Customer Segmentation Using Clustering Algorithms ⭐ 8

Customer Segmentation Using Unsupervised Machine Learning Algorithms

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