explainable-ml | Topic | Ecosyste.ms: Repos

Topic: "explainable-ml"

jacobgil/pytorch-grad-cam

Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

Language: Python - Size: 134 MB - Last synced at: about 1 month ago - Pushed at: 3 months ago - Stars: 11,641 - Forks: 1,636

interpretml/interpret

Fit interpretable models. Explain blackbox machine learning.

Language: C++ - Size: 14.9 MB - Last synced at: 3 days ago - Pushed at: 8 days ago - Stars: 6,536 - Forks: 752

jphall663/awesome-machine-learning-interpretability

A curated list of awesome responsible machine learning resources.

Size: 3.2 MB - Last synced at: 5 days ago - Pushed at: 5 days ago - Stars: 3,805 - Forks: 604

microsoft/tensorwatch

Debugging, monitoring and visualization for Python Machine Learning and Data Science

Language: Jupyter Notebook - Size: 21.2 MB - Last synced at: 7 days ago - Pushed at: almost 2 years ago - Stars: 3,446 - Forks: 362

MAIF/shapash

🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

Language: Jupyter Notebook - Size: 61.6 MB - Last synced at: 14 days ago - Pushed at: 14 days ago - Stars: 2,893 - Forks: 348

truera/trulens

Evaluation and Tracking for LLM Experiments and AI Agents

Language: Python - Size: 344 MB - Last synced at: 1 day ago - Pushed at: 1 day ago - Stars: 2,586 - Forks: 217

AstraZeneca/awesome-explainable-graph-reasoning

A collection of research papers and software related to explainability in graph machine learning.

Size: 1.7 MB - Last synced at: 1 day ago - Pushed at: about 3 years ago - Stars: 1,966 - Forks: 131

divelab/DIG

A library for graph deep learning research

Language: Python - Size: 264 MB - Last synced at: 2 days ago - Pushed at: 12 months ago - Stars: 1,962 - Forks: 288

Trusted-AI/AIX360

Interpretability and explainability of data and machine learning models

Language: Python - Size: 358 MB - Last synced at: 29 days ago - Pushed at: 4 months ago - Stars: 1,698 - Forks: 312

microsoft/responsible-ai-toolbox

Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.

Language: TypeScript - Size: 111 MB - Last synced at: 7 days ago - Pushed at: 5 months ago - Stars: 1,563 - Forks: 414

csinva/imodels

Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).

Language: Jupyter Notebook - Size: 162 MB - Last synced at: 11 days ago - Pushed at: 24 days ago - Stars: 1,467 - Forks: 125

ModelOriented/DALEX

moDel Agnostic Language for Exploration and eXplanation

Language: Python - Size: 798 MB - Last synced at: about 7 hours ago - Pushed at: 4 months ago - Stars: 1,428 - Forks: 168

interpretml/DiCE

Generate Diverse Counterfactual Explanations for any machine learning model.

Language: Python - Size: 14.9 MB - Last synced at: 26 days ago - Pushed at: 26 days ago - Stars: 1,405 - Forks: 203

EthicalML/xai

XAI - An eXplainability toolbox for machine learning

Language: Python - Size: 17.8 MB - Last synced at: 3 days ago - Pushed at: over 3 years ago - Stars: 1,179 - Forks: 179

salesforce/OmniXAI

OmniXAI: A Library for eXplainable AI

Language: Jupyter Notebook - Size: 64.7 MB - Last synced at: 28 days ago - Pushed at: 11 months ago - Stars: 924 - Forks: 99

deel-ai/xplique

👋 Xplique is a Neural Networks Explainability Toolbox

Language: Python - Size: 33.4 MB - Last synced at: 2 days ago - Pushed at: 9 months ago - Stars: 689 - Forks: 58

ScalaConsultants/Aspect-Based-Sentiment-Analysis

💭 Aspect-Based-Sentiment-Analysis: Transformer & Explainable ML (TensorFlow)

Language: Python - Size: 1.8 MB - Last synced at: 29 days ago - Pushed at: about 2 months ago - Stars: 570 - Forks: 90

h2oai/mli-resources

H2O.ai Machine Learning Interpretability Resources

Language: Jupyter Notebook - Size: 65.8 MB - Last synced at: about 1 month ago - Pushed at: over 4 years ago - Stars: 488 - Forks: 130

explainX/explainx

Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code. We are looking for co-authors to take this project forward. Reach out @ [email protected]

Language: Jupyter Notebook - Size: 61.3 MB - Last synced at: about 1 month ago - Pushed at: 10 months ago - Stars: 436 - Forks: 56

mdipietro09/DataScience_ArtificialIntelligence_Utils

Examples of Data Science projects and Artificial Intelligence use-cases

Language: Jupyter Notebook - Size: 233 MB - Last synced at: over 1 year ago - Pushed at: almost 2 years ago - Stars: 402 - Forks: 288

keisen/tf-keras-vis

Neural network visualization toolkit for tf.keras

Language: Python - Size: 97.1 MB - Last synced at: 28 days ago - Pushed at: 4 months ago - Stars: 325 - Forks: 45

carla-recourse/CARLA

CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms

Language: Python - Size: 1.89 MB - Last synced at: about 1 month ago - Pushed at: over 1 year ago - Stars: 288 - Forks: 64

ashutosh1919/explainable-cnn

📦 PyTorch based visualization package for generating layer-wise explanations for CNNs.

Language: Python - Size: 3.99 MB - Last synced at: about 7 hours ago - Pushed at: almost 2 years ago - Stars: 230 - Forks: 34

benedekrozemberczki/shapley

The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).

Language: Python - Size: 826 KB - Last synced at: about 1 month ago - Pushed at: almost 2 years ago - Stars: 220 - Forks: 35

mims-harvard/GraphXAI

GraphXAI: Resource to support the development and evaluation of GNN explainers

Language: Python - Size: 249 MB - Last synced at: 8 months ago - Pushed at: about 1 year ago - Stars: 166 - Forks: 29

NorskRegnesentral/shapr

Explaining the output of machine learning models with more accurately estimated Shapley values

Language: HTML - Size: 107 MB - Last synced at: 7 days ago - Pushed at: 7 days ago - Stars: 160 - Forks: 36

AstraZeneca/awesome-shapley-value

Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)

Size: 622 KB - Last synced at: 2 days ago - Pushed at: almost 3 years ago - Stars: 152 - Forks: 12

JHoelli/Awesome-Time-Series-Explainability

A list of (post-hoc) XAI for time series

Size: 424 KB - Last synced at: 6 days ago - Pushed at: 10 months ago - Stars: 147 - Forks: 18

fzi-forschungszentrum-informatik/TSInterpret

An Open-Source Library for the interpretability of time series classifiers

Language: Python - Size: 200 MB - Last synced at: 10 days ago - Pushed at: 7 months ago - Stars: 134 - Forks: 15

bgreenwell/fastshap

Fast approximate Shapley values in R

Language: R - Size: 99.4 MB - Last synced at: 3 days ago - Pushed at: about 1 month ago - Stars: 123 - Forks: 18

dylan-slack/TalkToModel

TalkToModel gives anyone with the powers of XAI through natural language conversations 💬!

Language: Python - Size: 10.1 MB - Last synced at: 2 months ago - Pushed at: almost 2 years ago - Stars: 120 - Forks: 25

ajayarunachalam/Deep_XF

Package towards building Explainable Forecasting and Nowcasting Models with State-of-the-art Deep Neural Networks and Dynamic Factor Model on Time Series data sets with single line of code. Also, provides utilify facility for time-series signal similarities matching, and removing noise from timeseries signals.

Language: Jupyter Notebook - Size: 27.5 MB - Last synced at: 20 days ago - Pushed at: over 2 years ago - Stars: 118 - Forks: 24

d909b/cxplain

Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.

Language: Python - Size: 262 KB - Last synced at: over 1 year ago - Pushed at: almost 5 years ago - Stars: 118 - Forks: 34

ModelOriented/survex

Explainable Machine Learning in Survival Analysis

Language: R - Size: 309 MB - Last synced at: 29 days ago - Pushed at: about 1 year ago - Stars: 112 - Forks: 10

andreysharapov/xaience

All about explainable AI, algorithmic fairness and more

Language: HTML - Size: 7.81 GB - Last synced at: about 1 year ago - Pushed at: almost 2 years ago - Stars: 106 - Forks: 12

salimamoukou/acv00

ACV is a python library that provides explanations for any machine learning model or data. It gives local rule-based explanations for any model or data and different Shapley Values for tree-based models.

Language: Jupyter Notebook - Size: 19 MB - Last synced at: 12 days ago - Pushed at: almost 3 years ago - Stars: 102 - Forks: 11

M-Nauta/ProtoTree

ProtoTrees: Neural Prototype Trees for Interpretable Fine-grained Image Recognition, published at CVPR2021

Language: Python - Size: 870 KB - Last synced at: about 2 months ago - Pushed at: almost 3 years ago - Stars: 101 - Forks: 21

whyisyoung/CADE

Code for our USENIX Security 2021 paper -- CADE: Detecting and Explaining Concept Drift Samples for Security Applications

Language: Python - Size: 188 MB - Last synced at: over 1 year ago - Pushed at: over 2 years ago - Stars: 99 - Forks: 31

awslabs/sagemaker-explaining-credit-decisions

Amazon SageMaker Solution for explaining credit decisions.

Language: Python - Size: 2.77 MB - Last synced at: about 1 month ago - Pushed at: about 2 years ago - Stars: 97 - Forks: 28

charmlab/mace

Model Agnostic Counterfactual Explanations

Language: Python - Size: 3 MB - Last synced at: 5 months ago - Pushed at: over 2 years ago - Stars: 87 - Forks: 12

ModelOriented/fairmodels

Flexible tool for bias detection, visualization, and mitigation

Language: R - Size: 142 MB - Last synced at: 27 days ago - Pushed at: 5 months ago - Stars: 86 - Forks: 16

12wang3/rrl

The code of NeurIPS 2021 paper "Scalable Rule-Based Representation Learning for Interpretable Classification" and TPAMI paper "Learning Interpretable Rules for Scalable Data Representation and Classification"

Language: Python - Size: 561 KB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 84 - Forks: 22

kiraving/SegGradCAM

SEG-GRAD-CAM: Interpretable Semantic Segmentation via Gradient-Weighted Class Activation Mapping

Language: Jupyter Notebook - Size: 316 MB - Last synced at: almost 2 years ago - Pushed at: almost 2 years ago - Stars: 83 - Forks: 17

firmai/ml-fairness-framework

FairPut - Machine Learning Fairness Framework with LightGBM — Explainability, Robustness, Fairness (by @firmai)

Language: Jupyter Notebook - Size: 10.5 MB - Last synced at: 7 days ago - Pushed at: over 3 years ago - Stars: 71 - Forks: 16

xplainable/xplainable

Real-time explainable machine learning for business optimisation

Language: Python - Size: 20.8 MB - Last synced at: 8 days ago - Pushed at: 8 days ago - Stars: 61 - Forks: 6

neemakot/Health-Fact-Checking 📦

Dataset and code for "Explainable Automated Fact-Checking for Public Health Claims" from EMNLP 2020.

Language: Python - Size: 23.7 MB - Last synced at: 3 months ago - Pushed at: about 4 years ago - Stars: 60 - Forks: 10

wilsonjr/ClusterShapley

Explaining dimensionality results using SHAP values

Language: C++ - Size: 655 KB - Last synced at: 22 days ago - Pushed at: 5 months ago - Stars: 54 - Forks: 6

pterhoer/ExplainableFaceImageQuality

Pixel-Level Face Image Quality Assessment for Explainable Face Recognition

Language: Python - Size: 1.87 MB - Last synced at: 6 months ago - Pushed at: 6 months ago - Stars: 53 - Forks: 9

rehmanzafar/xai-iml-sota

Interesting resources related to Explainable Artificial Intelligence, Interpretable Machine Learning, Interactive Machine Learning, Human in Loop and Visual Analytics.

Language: R - Size: 1.12 MB - Last synced at: over 2 years ago - Pushed at: over 2 years ago - Stars: 51 - Forks: 13

wangyongjie-ntu/CFAI

A collection of algorithms of counterfactual explanations.

Language: Python - Size: 8.97 MB - Last synced at: 3 days ago - Pushed at: over 4 years ago - Stars: 50 - Forks: 9

adaamko/POTATO

XAI based human-in-the-loop framework for automatic rule-learning.

Language: Jupyter Notebook - Size: 6.07 MB - Last synced at: 23 days ago - Pushed at: 12 months ago - Stars: 49 - Forks: 8

hungntt/xai_thyroid

Classification and Object Detection XAI methods (CAM-based, backpropagation-based, perturbation-based, statistic-based) for thyroid cancer ultrasound images

Language: Python - Size: 2.01 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 45 - Forks: 6

microsoft/responsible-ai-workshop

Responsible AI Workshop: a series of tutorials & walkthroughs to illustrate how put responsible AI into practice

Language: Jupyter Notebook - Size: 237 MB - Last synced at: 7 days ago - Pushed at: 4 months ago - Stars: 44 - Forks: 9

rikhuijzer/SIRUS.jl

Interpretable Machine Learning via Rule Extraction

Language: Julia - Size: 1.35 MB - Last synced at: 11 days ago - Pushed at: 9 months ago - Stars: 37 - Forks: 2

BirkhoffG/Explainable-ML-Papers

A list of research papers of explainable machine learning.

Size: 13.7 KB - Last synced at: about 2 months ago - Pushed at: about 4 years ago - Stars: 36 - Forks: 3

akarasman/yolo-heatmaps

A utility for generating heatmaps of YOLOv8 using Layerwise Relevance Propagation (LRP/CRP).

Language: Jupyter Notebook - Size: 3.68 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 35 - Forks: 11

capitalone/global-attribution-mapping

GAM (Global Attribution Mapping) explains the landscape of neural network predictions across subpopulations

Language: Python - Size: 3.64 MB - Last synced at: 1 day ago - Pushed at: 2 months ago - Stars: 34 - Forks: 25

charmlab/recourse

Code to reproduce our paper on probabilistic algorithmic recourse: https://arxiv.org/abs/2006.06831

Language: Python - Size: 1.14 MB - Last synced at: about 1 year ago - Pushed at: over 2 years ago - Stars: 34 - Forks: 5

safreita1/malnet-image

A large-scale database of malicious software images

Language: Python - Size: 1.73 MB - Last synced at: almost 2 years ago - Pushed at: almost 4 years ago - Stars: 34 - Forks: 9

fxs130430/SHAP_FOLD

(Explainable AI) - Learning Non-Monotonic Logic Programs From Statistical Models Using High-Utility Itemset Mining

Language: Prolog - Size: 7.31 MB - Last synced at: over 2 years ago - Pushed at: about 5 years ago - Stars: 34 - Forks: 8

koo-ec/Awesome-LLM-Explainability

A curated list of explainability-related papers, articles, and resources focused on Large Language Models (LLMs). This repository aims to provide researchers, practitioners, and enthusiasts with insights into the explainability implications, challenges, and advancements surrounding these powerful models.

Size: 786 KB - Last synced at: about 16 hours ago - Pushed at: about 17 hours ago - Stars: 33 - Forks: 1

UTS-CASLab/hyperbox-brain

A scikit-learn compatible hyperbox-based machine learning library in Python

Language: Python - Size: 8.45 MB - Last synced at: 9 days ago - Pushed at: 6 months ago - Stars: 33 - Forks: 2

willbakst/pytorch-lattice

A PyTorch implementation of constrained optimization and modeling techniques

Language: Python - Size: 1.19 MB - Last synced at: 13 days ago - Pushed at: about 1 year ago - Stars: 31 - Forks: 2

divyat09/cf-feasibility

Code accompanying the paper "Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers"

Language: Python - Size: 38.3 MB - Last synced at: 21 days ago - Pushed at: over 2 years ago - Stars: 31 - Forks: 10

lucko515/cnn-raccoon

Create interactive dashboards for your Convolutional Neural Networks with a single line of code!

Language: Python - Size: 5.4 MB - Last synced at: 2 months ago - Pushed at: over 4 years ago - Stars: 31 - Forks: 2

pyartemis/artemis

A Python package with explanation methods for extraction of feature interactions from predictive models

Language: Jupyter Notebook - Size: 16.6 MB - Last synced at: 20 days ago - Pushed at: over 1 year ago - Stars: 30 - Forks: 0

marvinbuss/ExplainableML-Vision

This repository introduces different Explainable AI approaches and demonstrates how they can be implemented with PyTorch and torchvision. Used approaches are Class Activation Mappings, LIMA and SHapley Additive exPlanations.

Language: Jupyter Notebook - Size: 52.4 MB - Last synced at: 3 months ago - Pushed at: almost 3 years ago - Stars: 30 - Forks: 5

Networks-Learning/strategic-decisions

Code and data for decision making under strategic behavior, NeurIPS 2020 & Management Science 2024.

Language: Jupyter Notebook - Size: 5.94 MB - Last synced at: 2 months ago - Pushed at: over 1 year ago - Stars: 29 - Forks: 5

KaryFramling/py-ciu

Explainable Artificial Intelligence through Contextual Importance and Utility

Language: Jupyter Notebook - Size: 27.4 MB - Last synced at: 11 days ago - Pushed at: 10 months ago - Stars: 28 - Forks: 8

jphall663/diabetes_use_case

Sample use case for Xavier AI in Healthcare conference: https://www.xavierhealth.org/ai-summit-day2/

Language: Jupyter Notebook - Size: 73.7 MB - Last synced at: 3 months ago - Pushed at: about 1 year ago - Stars: 27 - Forks: 13

ilaspltd/ILASP-releases

Size: 1.95 KB - Last synced at: almost 2 years ago - Pushed at: almost 2 years ago - Stars: 26 - Forks: 0

Crisp-Unimib/ContrXT

a tool for comparing the predictions of any text classifiers

Language: Python - Size: 6.4 MB - Last synced at: about 1 month ago - Pushed at: almost 3 years ago - Stars: 25 - Forks: 2

rehmanzafar/dlime_experiments

In this work, we propose a deterministic version of Local Interpretable Model Agnostic Explanations (LIME) and the experimental results on three different medical datasets shows the superiority for Deterministic Local Interpretable Model-Agnostic Explanations (DLIME).

Language: Jupyter Notebook - Size: 5.21 MB - Last synced at: over 1 year ago - Pushed at: almost 2 years ago - Stars: 24 - Forks: 15

giacoballoccu/explanation-quality-recsys

Post Processing Explanations Paths in Path Reasoning Recommender Systems with Knowledge Graphs

Language: Python - Size: 900 KB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 23 - Forks: 6

sametcopur/treemind

treemind interprets ensemble tree models by analyzing individual trees and their predictions, providing insights into the decision-making process.

Language: Python - Size: 373 MB - Last synced at: 2 days ago - Pushed at: 2 days ago - Stars: 22 - Forks: 2

ahmed-mohamed-sn/ATgfe

Automated Transparent Genetic Feature Engineering

Size: 2.19 MB - Last synced at: 9 days ago - Pushed at: almost 2 years ago - Stars: 22 - Forks: 5

declare-lab/identifiable-transformers

Language: Python - Size: 11.1 MB - Last synced at: 2 months ago - Pushed at: over 2 years ago - Stars: 22 - Forks: 2

jphall663/hc_ml

Slides, videos and other potentially useful artifacts from various presentations on responsible machine learning.

Language: TeX - Size: 34.4 MB - Last synced at: 27 days ago - Pushed at: over 5 years ago - Stars: 22 - Forks: 8

daikikatsuragawa/awesome-counterfactual-explanations

This repository is a curated collection of information (keywords, papers, libraries, books, etc.) about counterfactual explanations🙃 Contributions are welcome! Our maintenance capacity is limited, so we highly appreciate pull requests.

Size: 12.7 KB - Last synced at: 14 days ago - Pushed at: over 2 years ago - Stars: 21 - Forks: 0

nitishabharathi/LEMNA

Source code for 'Lemna: Explaining deep learning based security applications'.

Language: Python - Size: 1.86 MB - Last synced at: over 1 year ago - Pushed at: about 5 years ago - Stars: 21 - Forks: 8

jpmorganchase/cf-shap

Counterfactual SHAP: a framework for counterfactual feature importance

Language: HTML - Size: 823 KB - Last synced at: 14 days ago - Pushed at: almost 2 years ago - Stars: 20 - Forks: 9

HelmholtzAI-Consultants-Munich/PySDDR

A python package for semi-structured deep distributional regression

Language: Jupyter Notebook - Size: 8.83 MB - Last synced at: about 1 year ago - Pushed at: over 2 years ago - Stars: 20 - Forks: 8

ottenbreit-data-science/aplr

APLR builds predictive, interpretable regression and classification models using Automatic Piecewise Linear Regression. It often rivals tree-based methods in predictive accuracy while offering smoother and interpretable predictions.

Language: C++ - Size: 6.13 MB - Last synced at: about 1 month ago - Pushed at: 2 months ago - Stars: 19 - Forks: 5

markusorsi/mapchiral

Chiral version of the MinHashed Atom-Pair Fingerprint

Language: Python - Size: 323 KB - Last synced at: 6 months ago - Pushed at: 6 months ago - Stars: 19 - Forks: 5

s-marton/SYMPOL

(ICLR 2025) Mitigating Information Loss in Tree-Based Reinforcement Learning via Direct Optimization

Language: Python - Size: 746 KB - Last synced at: 30 days ago - Pushed at: 10 months ago - Stars: 19 - Forks: 1

benjaminpatrickevans/XAI

Genetic programming method for explaining complex black-box models

Language: Python - Size: 73.2 KB - Last synced at: 3 days ago - Pushed at: about 1 year ago - Stars: 19 - Forks: 3

12wang3/mllp

The code of AAAI 2020 paper "Transparent Classification with Multilayer Logical Perceptrons and Random Binarization".

Language: Python - Size: 3.69 MB - Last synced at: about 1 year ago - Pushed at: over 1 year ago - Stars: 19 - Forks: 6

CoderPat/learning-scaffold

This is the official implementation for the paper "Learning to Scaffold: Optimizing Model Explanations for Teaching"

Language: Jupyter Notebook - Size: 3.75 MB - Last synced at: 2 months ago - Pushed at: about 3 years ago - Stars: 19 - Forks: 5

DelTA-Lab-IITK/U-CAM

Visual Explanation using Uncertainty based Class Activation Maps

Language: Python - Size: 4.21 MB - Last synced at: almost 2 years ago - Pushed at: over 5 years ago - Stars: 19 - Forks: 3

pranoy-panda/Causal-Feature-Subset-Selection

Official repository for the paper "Instance-wise Causal Feature Selection for Model Interpretation" (CVPRW 2021)

Language: Python - Size: 178 KB - Last synced at: 5 months ago - Pushed at: about 4 years ago - Stars: 18 - Forks: 4

utwente-dmb/xai-papers

Language: TypeScript - Size: 147 MB - Last synced at: about 2 months ago - Pushed at: 11 months ago - Stars: 17 - Forks: 9

crunchiness/lernd

Lernd is ∂ILP (dILP) framework implementation based on Deepmind's paper Learning Explanatory Rules from Noisy Data.

Language: Python - Size: 162 KB - Last synced at: about 2 years ago - Pushed at: over 2 years ago - Stars: 17 - Forks: 4

MarcelRobeer/explabox

Explore/examine/explain/expose your model with the explabox!

Language: Python - Size: 3.05 MB - Last synced at: 5 days ago - Pushed at: about 2 months ago - Stars: 16 - Forks: 0

awojna/Rseslib

Data structures, algorithms and tools for rough sets, machine learning and data mining, including algorithms for discernibility matrix, reducts, decision rules, classification (KNN, NeuralNet, AQ15, RoughSet, RIONIDA, SVM, C4.5, and many others), discretization (1R, EntropyMinimization, ChiMerge, MD), and tool for explainable and interactive ML.

Language: Java - Size: 1.72 MB - Last synced at: 2 months ago - Pushed at: 2 months ago - Stars: 16 - Forks: 5