feature-importance

Model interpretability and understanding for PyTorch

XAI - An eXplainability toolbox for machine learning

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

Leave One Feature Out Importance

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

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.

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

An R package for computing asymmetric Shapley values to assess causality in any trained machine learning model

In this project I aim to apply Various Predictive Maintenance Techniques to accurately predict the impending failure of an aircraft turbofan engine.

A Julia package for interpretable machine learning with stochastic Shapley values

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

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

Variance-based Feature Importance in Neural Networks

PyRasgo

CancelOut is a layer for deep neural networks, that can help identify a subset of relevant input features for streaming or static data.

Awesome papers on Feature Selection

Official repository of the paper "Interpretable Anomaly Detection with DIFFI: Depth-based Isolation Forest Feature Importance", M. Carletti, M. Terzi, G. A. Susto.

A Rust implementation of fANOVA (functional analysis of variance)

Implementation of various feature selection methods using TensorFlow library.

Analyzing the Features which leads to heart diseases and visualizing the models' performance and important features using eli5, shap and pdp.

Contact: Alexander Hartl, Maximilian Bachl, Fares Meghdouri. Explainability methods and Adversarial Robustness metrics for RNNs for Intrusion Detection Systems. Also contains code for "SparseIDS: Learning Packet Sampling with Reinforcement Learning" (branch "rl").

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.

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

OncoNetExplainer: Explainable Prediction of Cancer Types Based on Gene Expression Data

Predicting the ideological direction of Supreme Court decisions: ensemble vs. unified case-based model

Using / reproducing TRIM from the paper "Transformation Importance with Applications to Cosmology" 🌌 (ICLR Workshop 2020)

This is a custom library for data processing, visualization and machine learning tools.

Extract textual information from Amazon products reviews and draw correlations through regression and fluctuation analysis.

Feature importance by the permutation method (for fastai V1)

The given information of network connection, model predicts if connection has some intrusion or not. Binary classification for good and bad type of the connection further converting to multi-class classification and most prominent is feature importance analysis.