explainable-ml | Topic | Ecosyste.ms: Repos

Topic: "explainable-ml"

danyvarghese/PyGol

A novel Inductive Logic Programming(ILP) system based on Meta Inverse Entailment in Python.

Language: C - Size: 6.9 MB - Last synced at: 3 months ago - Pushed at: 3 months ago - Stars: 13 - Forks: 3

h2oai/article-information-2019

Article for Special Edition of Information: Machine Learning with Python

Language: Jupyter Notebook - Size: 333 MB - Last synced at: 3 months ago - Pushed at: 6 months ago - Stars: 13 - Forks: 3

AI-vidence/antakia

AntakIA is THE tool to explain an ML model or replace it with a collection of basic explainable models.

Language: Python - Size: 188 MB - Last synced at: about 1 month ago - Pushed at: 12 months ago - Stars: 13 - Forks: 0

pnxenopoulos/cav-keras

Concept activation vectors for Keras

Language: Python - Size: 23 MB - Last synced at: over 2 years ago - Pushed at: over 2 years ago - Stars: 13 - Forks: 6

bifold-pathomics/xMIL

xMIL: Insightful Explanations for Multiple Instance Learning in Histopathology

Language: Jupyter Notebook - Size: 29.3 MB - Last synced at: 4 months ago - Pushed at: 4 months ago - Stars: 12 - Forks: 2

manikyabard/DashAI

DashAI provides a simple graphical user interface (GUI) that guides users through a step-by-step process through creating, training, and saving a model.

Language: Python - Size: 390 MB - Last synced at: 3 months ago - Pushed at: over 2 years ago - Stars: 12 - Forks: 6

prclibo/ice

Interpretable Control Exploration and Counterfactual Explanation (ICE) on StyleGAN

Language: Jupyter Notebook - Size: 8.43 MB - Last synced at: about 2 years ago - Pushed at: over 3 years ago - Stars: 12 - Forks: 6

Chacha-Chen/Explanations-Human-Studies

This repository provides a summarization of recent empirical studies/human studies that measure human understanding with machine explanations in human-AI interactions.

Size: 425 KB - Last synced at: 11 months ago - Pushed at: 11 months ago - Stars: 11 - Forks: 0

Broundal/Pytolemaic

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

Language: Python - Size: 1.16 MB - Last synced at: 19 days ago - Pushed at: about 1 year ago - Stars: 11 - Forks: 3

jpmorganchase/cf-shap-facct22

Counterfactual Shapley Additive Explanation: Experiments

Language: Jupyter Notebook - Size: 1.34 MB - Last synced at: 15 days ago - Pushed at: almost 2 years ago - Stars: 11 - Forks: 7

tangxianfeng/FATE

Implementation of "Knowing your FATE: Friendship, Action and Temporal Explanations for User Engagement Prediction on Social Apps"

Language: Python - Size: 5.86 KB - Last synced at: over 2 years ago - Pushed at: over 5 years ago - Stars: 11 - Forks: 4

gianluigilopardo/smace

Code for the paper "SMACE: A New Method for the Interpretability of Composite Decision Systems", ECML 2022

Language: Jupyter Notebook - Size: 806 KB - Last synced at: 22 days ago - Pushed at: about 2 years ago - Stars: 10 - Forks: 1

Minimize the risks and maximize the benefits of using data-driven technologies within government processes, programs and services through transparency. | Réduire les risques et à maximiser les avantages liés à l’utilisation de technologies axées sur les données, dans le cadre de processus, programmes et services gouvernementaux, grâce à la transparence.

Size: 469 KB - Last synced at: about 1 year ago - Pushed at: about 4 years ago - Stars: 10 - Forks: 1

yuvalailer/nnplot

:tv: A Python library for pruning and visualizing Keras Neural Networks' structure and weights

Language: Python - Size: 10 MB - Last synced at: 21 days ago - Pushed at: over 5 years ago - Stars: 10 - Forks: 1

LamineTourelab/Explainable-AI

In this repository you will fine explainability of machine learning models.

Size: 8.79 KB - Last synced at: 25 days ago - Pushed at: over 2 years ago - Stars: 9 - Forks: 0

lucasdavid/keras-explainable

Efficient explaining AI algorithms for Keras models

Language: Python - Size: 87.9 MB - Last synced at: about 2 years ago - Pushed at: over 2 years ago - Stars: 9 - Forks: 1

JonathanCrabbe/CARs

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.

Language: Python - Size: 2.86 MB - Last synced at: about 1 year ago - Pushed at: over 2 years ago - Stars: 9 - Forks: 2

mlpapers/interpretability

Awesome papers on Interpretable Machine Learning

Size: 3.91 KB - Last synced at: 3 days ago - Pushed at: over 4 years ago - Stars: 9 - Forks: 0

jphall663/jsm_2018_paper

Paper for 2018 Joint Statistical Meetings: https://ww2.amstat.org/meetings/jsm/2018/onlineprogram/AbstractDetails.cfm?abstractid=329539

Language: TeX - Size: 12.7 MB - Last synced at: 3 months ago - Pushed at: over 6 years ago - Stars: 9 - Forks: 2

kaspersgit/ml_2_sql

Automating machine learning training and save an SQL version of the model

Language: Python - Size: 17.4 MB - Last synced at: 26 days ago - Pushed at: 3 months ago - Stars: 8 - Forks: 2

csinva/clinical-rule-development

Building and vetting clinical decision rules.

Language: Jupyter Notebook - Size: 160 MB - Last synced at: about 2 months ago - Pushed at: 3 months ago - Stars: 8 - Forks: 2

piotromashov/baycon

Research project on generation of counterfactuals for eXplainable AI, based on Bayesian Generation

Language: Python - Size: 4.37 MB - Last synced at: 6 months ago - Pushed at: 6 months ago - Stars: 8 - Forks: 0

Optum/long-medical-document-lms

Explain and train language models that extract information from long medical documents with the Masked Sampling Procedure (MSP)

Language: Jupyter Notebook - Size: 27 MB - Last synced at: 2 months ago - Pushed at: 11 months ago - Stars: 7 - Forks: 0

Batev/XAI-Analytics

XAI-Analytics is a tool that opens the black-box of machine learning. It helps the user to understand the decision-making process of machine learning models.

Language: HTML - Size: 78 MB - Last synced at: about 2 years ago - Pushed at: about 2 years ago - Stars: 7 - Forks: 2

forestry-labs/distillML

An R package providing functions for interpreting and distilling machine learning models

Language: R - Size: 9.76 MB - Last synced at: 2 months ago - Pushed at: about 2 years ago - Stars: 7 - Forks: 2

donlapark/XLabel

XLabel: An Explainable Data Labeling Assistant

Language: Jupyter Notebook - Size: 2.84 MB - Last synced at: about 2 months ago - Pushed at: over 2 years ago - Stars: 7 - Forks: 2

zmlabe/ExtremeEvents

Using ANN's to reveal changes in extreme events and internal variability in climate models

Language: Python - Size: 414 KB - Last synced at: about 1 year ago - Pushed at: over 4 years ago - Stars: 7 - Forks: 8

EloiZ/awesome_explainable_driving

A curated list of papers on explainability and interpretability of self-driving models

Size: 3.91 KB - Last synced at: 12 days ago - Pushed at: over 4 years ago - Stars: 7 - Forks: 0

edahelsinki/pyslise

Robust regression algorithm that can be used for explaining black box models (Python implementation)

Language: Python - Size: 3.55 MB - Last synced at: about 1 month ago - Pushed at: about 1 year ago - Stars: 6 - Forks: 1

Networks-Learning/counterfactual-continuous-mdp

Code for "Finding Counterfactually Optimal Action Sequences in Continuous State Spaces", NeurIPS 2023.

Language: Python - Size: 85.9 KB - Last synced at: 26 days ago - Pushed at: over 1 year ago - Stars: 6 - Forks: 1

SonghuaHu-UMD/Explainable_AI_Comparison

Language: Python - Size: 16.7 MB - Last synced at: 7 days ago - Pushed at: over 2 years ago - Stars: 6 - Forks: 3

nphdang/DeepCoDA

Deep learning for personalized interpretability for compositional health data

Language: Python - Size: 10.8 MB - Last synced at: about 2 years ago - Pushed at: about 3 years ago - Stars: 6 - Forks: 1

IoBT-VISTEC/PPMI_DL

Parkinson's Disease Recognition Using SPECT Image and Interpretable AI: A Tutorial. The interpretation of the deep learning model to analyze the prediction results of 3D-images data.

Language: Python - Size: 301 KB - Last synced at: over 2 years ago - Pushed at: about 4 years ago - Stars: 6 - Forks: 1

IBMDeveloperUK/AIX360-Introduction

Introduction to explaining data and machine learning models with aif360

Language: Jupyter Notebook - Size: 3.34 MB - Last synced at: over 2 years ago - Pushed at: almost 5 years ago - Stars: 6 - Forks: 4

aws-samples/amazon-sagemaker-autopilot-feature-engineering-transformer-and-model-explainability

This repository contains sample code to generate SHAP plots out of the SageMaker autopilot.

Language: Jupyter Notebook - Size: 700 KB - Last synced at: 22 days ago - Pushed at: almost 5 years ago - Stars: 6 - Forks: 6

tpoisot/InterpretableSDMWithJulia

Slides for the "Interpretable SDM with Julia" workshop

Language: TeX - Size: 268 MB - Last synced at: 3 months ago - Pushed at: 8 months ago - Stars: 5 - Forks: 2

mfumagalli68/xi-method

Xi method

Language: Python - Size: 2.82 MB - Last synced at: about 1 month ago - Pushed at: almost 2 years ago - Stars: 5 - Forks: 1

edahelsinki/slise

Robust regression algorithm that can be used for explaining black box models (R implementation)

Language: R - Size: 3.72 MB - Last synced at: about 1 month ago - Pushed at: almost 2 years ago - Stars: 5 - Forks: 1

CeciPani/MARLENA

A python library to agnostically explain multi-label black-box classifiers (tabular data)

Language: Jupyter Notebook - Size: 4.61 MB - Last synced at: over 2 years ago - Pushed at: over 2 years ago - Stars: 5 - Forks: 0

jnikhilreddy/Explainable-AI-papers-Year-wise

List of papers in the area of Explainable Artificial Intelligence Year wise

Size: 18.6 KB - Last synced at: over 2 years ago - Pushed at: over 5 years ago - Stars: 5 - Forks: 1

gudovskiy/e2x

Explain to Fix: A Framework to Interpret and Correct DNN Object Detector Predictions

Language: C++ - Size: 110 KB - Last synced at: over 2 years ago - Pushed at: over 6 years ago - Stars: 5 - Forks: 0

fixouttech/fixout

Algorithmic inspection for trustworthy ML models

Language: Python - Size: 10.7 MB - Last synced at: 21 days ago - Pushed at: about 2 months ago - Stars: 4 - Forks: 0

zalkikar/mlm-bias

Measuring Biases in Masked Language Models for PyTorch Transformers. Support for multiple social biases and evaluation measures.

Language: Python - Size: 45.9 KB - Last synced at: about 1 month ago - Pushed at: 6 months ago - Stars: 4 - Forks: 2

machinelearningnuremberg/INN

Explainable deep networks that are not only as accurate as their black-box deep-learning counterparts but also as interpretable as state-of-the-art explanation techniques.

Language: Python - Size: 60.5 KB - Last synced at: 11 months ago - Pushed at: over 1 year ago - Stars: 4 - Forks: 0

qetdr/xAutoML-Project2

Explainable Automated Machine Learning Framework for Predicting the Risk of Major Adverse Cardiac Event (MACE)

Language: Jupyter Notebook - Size: 34.1 MB - Last synced at: 3 months ago - Pushed at: over 2 years ago - Stars: 4 - Forks: 2

statkclee/model

데이터 과학 모형

Language: HTML - Size: 116 MB - Last synced at: over 1 year ago - Pushed at: about 3 years ago - Stars: 4 - Forks: 8

ajsanjoaquin/mPerturb

Implementation of Interpretable Explanations of Black Boxes by Meaningful Perturbation (Fong, et. al., 2018)

Language: Jupyter Notebook - Size: 2.52 MB - Last synced at: over 2 years ago - Pushed at: about 3 years ago - Stars: 4 - Forks: 1

IBMDeveloperMEA/Trusted-AI-Build-Explainable-ML-Models-using-AIX360

Imagine boarding the Titanic in 2021, and you have provided all your details as a passenger to the captain. There is are three people involved, the data scientist, captain and the passenger. Imagine the company who has built Titanic has created a machine ML model to predict the rate of survival of the passengers, in case of a disaster. The job of the data scientist is to make a model that is explainable to the passengers who are not technical, and that they get the answer about the reasons why they may not survive.

Language: Jupyter Notebook - Size: 8.26 MB - Last synced at: over 2 years ago - Pushed at: over 3 years ago - Stars: 4 - Forks: 2

acmater/exmoset

Automating the generation of human readable descriptions of arbitrary subsets of molecular space.

Language: Python - Size: 21.9 MB - Last synced at: 29 days ago - Pushed at: almost 4 years ago - Stars: 4 - Forks: 0

Sidx369/Explainable-AI-Notebooks

Explainable AI (XAI) Notebooks

Language: Jupyter Notebook - Size: 585 KB - Last synced at: over 2 years ago - Pushed at: almost 4 years ago - Stars: 4 - Forks: 0

julienraffaud/ProtoDash

PySpark Implementation of the ProtoDash subset selection algorithm.

Language: Jupyter Notebook - Size: 767 KB - Last synced at: over 1 year ago - Pushed at: about 5 years ago - Stars: 4 - Forks: 1

EloiZ/awesome-contrastive-explanation

A curated list of awesome contrastive explanation in ML resources

Size: 18.6 KB - Last synced at: 1 day ago - Pushed at: over 5 years ago - Stars: 4 - Forks: 0

henrikbostrom/xrf

xrf is a Python package that implements random forests with example attribution

Language: Python - Size: 360 KB - Last synced at: 2 days ago - Pushed at: 2 days ago - Stars: 3 - Forks: 0

braindatalab/xai-tris

XAI-Tris

Language: Jupyter Notebook - Size: 1.14 MB - Last synced at: about 2 months ago - Pushed at: about 2 months ago - Stars: 3 - Forks: 2

h-fuzzy-logic/explainability-fairness-safety-for-ai

Resources to improve the explainability, fairness, and safety of your AI

Size: 9.44 MB - Last synced at: 3 months ago - Pushed at: 3 months ago - Stars: 3 - Forks: 1

bgreenwell/ebm

Explainable Boosting Machines

Language: R - Size: 44.5 MB - Last synced at: 18 days ago - Pushed at: 4 months ago - Stars: 3 - Forks: 1

Networks-Learning/human-aligned-calibration

Code for "Human-Aligned Calibration for AI-Assisted Decision Making", NeurIPS 2023

Language: Python - Size: 12.7 KB - Last synced at: 2 months ago - Pushed at: almost 2 years ago - Stars: 3 - Forks: 2

marcovirgolin/CoGS

A baseline genetic algorithm for the discovery of counterfactuals, implemented in Python for ease of use and heavily leveraging NumPy for speed.

Language: Python - Size: 402 KB - Last synced at: over 2 years ago - Pushed at: over 2 years ago - Stars: 3 - Forks: 1

wiese-m/survival-studio

Survival Studio - a tool for automatic and interactive exploration of complex survival models. The project is carried out as part of the master's thesis supervised by Przemyslaw Biecek.

Language: Python - Size: 3.22 MB - Last synced at: 4 months ago - Pushed at: over 2 years ago - Stars: 3 - Forks: 0

sumuzhao/Investigate-BERT-Non-linearity-Commutativity

Investigate BERT on Non-linearity and Layer Commutativity

Language: Python - Size: 228 KB - Last synced at: over 2 years ago - Pushed at: over 2 years ago - Stars: 3 - Forks: 0

GhadaElkhawaga/PPM_XAI_Comparison

Code of experiments implemented in the paper "Explainability of Predictive Process Monitoring results: Techniques, Experiments and Lessons Learned", comparing XAI methods at different granularities (global/local) with different settings on predictive process monitoring outcomes using process mining event logs

Language: HTML - Size: 59.7 MB - Last synced at: about 2 years ago - Pushed at: almost 3 years ago - Stars: 3 - Forks: 1

scottjingtt/awesome-interpretable-transfer-learning

Paper and resources collections about interpretable AI (XAI)

Size: 9.77 KB - Last synced at: 1 day ago - Pushed at: almost 3 years ago - Stars: 3 - Forks: 0

utkarsh512/ganbert Fork of crux82/ganbert

Enhancing the BERT training with Semi-supervised Generative Adversarial Networks and LIME visualizations

Language: Python - Size: 5.24 MB - Last synced at: 11 months ago - Pushed at: about 4 years ago - Stars: 3 - Forks: 0

nvedant07/effort_reward_fairness

Code for ICML 2019 paper titled "On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning"

Language: Python - Size: 998 KB - Last synced at: over 2 years ago - Pushed at: about 6 years ago - Stars: 3 - Forks: 2

japgarrido/Hackathon-The-Summer-Song-Oracle

Este proyecto, desarrollado para la Hackathon de Oracle 2025, busca predecir la popularidad de canciones para identificar la próxima canción del verano.

Language: Jupyter Notebook - Size: 46.5 MB - Last synced at: 22 days ago - Pushed at: 27 days ago - Stars: 2 - Forks: 0

rikardvinge/explainpolysvm

ExplainPolySVM is a python package to provide interpretation and explainability to Support Vector Machine models trained with polynomial kernels. The package can be used with any SVM model as long as the components of the model can be extracted.

Language: Python - Size: 30.4 MB - Last synced at: 15 days ago - Pushed at: about 2 months ago - Stars: 2 - Forks: 0

tomjanus/ghg_emissions_myanmar

Collection of notebooks accompanying a research paper on evaluating GHG emissions from hydroelectric, multipurpose and irrigation reservoirs in Myanmar

Language: Jupyter Notebook - Size: 3.67 MB - Last synced at: 7 months ago - Pushed at: 7 months ago - Stars: 2 - Forks: 0

pyladiesams/intro-to-explainabilty-in-finance-oct2024

Building a model is just one piece of the puzzle in data science; explaining how it works is just as important, especially in finance where transparency and explainability is key.

Language: Jupyter Notebook - Size: 7.48 MB - Last synced at: 3 months ago - Pushed at: 8 months ago - Stars: 2 - Forks: 2

PolyPhyHub/PolyGlot Fork of OskarElek/PolyGlot

Web app for visualizing language embeddings in 3D space using the novel MCPM metric.

Language: JavaScript - Size: 302 MB - Last synced at: 12 months ago - Pushed at: 12 months ago - Stars: 2 - Forks: 6

GiulioPr/Pcfi

Per Class Feature Importance (PCFI): an explainability method for decision tree classifiers.

Language: Jupyter Notebook - Size: 6.67 MB - Last synced at: 3 months ago - Pushed at: about 1 year ago - Stars: 2 - Forks: 0

craymichael/PostHocExplainerEvaluation

Evaluation framework for post hoc explanation methods | Explainable AI (XAI)

Language: Jupyter Notebook - Size: 14.5 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 2 - Forks: 0

fau-masters-collected-works-cgarbin/shap-experiments-image-classification

Exploring SHAP feature attribution for image classification

Language: Jupyter Notebook - Size: 26.3 MB - Last synced at: about 1 year ago - Pushed at: almost 2 years ago - Stars: 2 - Forks: 0

CompliancePal/modelcard-action

Validate the model card document in a GitHub action

Language: TypeScript - Size: 16.1 MB - Last synced at: 11 days ago - Pushed at: about 2 years ago - Stars: 2 - Forks: 0

fork123aniket/Model-agnostic-Graph-Explainability-from-Scratch

Implementation of Model-Agnostic Graph Explainability Technique from Scratch in PyTorch

Language: Python - Size: 3.08 MB - Last synced at: 4 months ago - Pushed at: about 2 years ago - Stars: 2 - Forks: 0

DAMO-DI-ML/Neurips2021-Submodular-Ruleset

Source code of NeurIPS'21 paper: Learning Interpretable Decision Rule Sets: A Submodular Optimization Approach

Language: Python - Size: 25.5 MB - Last synced at: over 2 years ago - Pushed at: over 2 years ago - Stars: 2 - Forks: 0

thekaranacharya/ai-visual-reasoning

A pipeline to explain any CNN Image Classification model outputs using a combination of GradCAM(visual) and Case-based Reasoning methods

Language: Jupyter Notebook - Size: 11.5 MB - Last synced at: almost 2 years ago - Pushed at: about 3 years ago - Stars: 2 - Forks: 1

verenablaschke/ma-thesis

Explainable Machine Learning in Linguistics and Applied NLP: Two Case Studies of Norwegian Dialectometry and Sexism Detection in French Tweets

Language: Python - Size: 3.23 MB - Last synced at: over 2 years ago - Pushed at: over 3 years ago - Stars: 2 - Forks: 1

xarion/EPM

Evaluation of Perturbation Methods for Deep Learning Explanation Methods

Language: Python - Size: 25.8 MB - Last synced at: almost 2 years ago - Pushed at: over 3 years ago - Stars: 2 - Forks: 0

LightnessOfBeing/ImpreciseSHAP

Implementation of the algorithm described in the paper "An Imprecise SHAP as a Tool for Explaining the Class Probability Distributions under Limited Training Data"

Language: Jupyter Notebook - Size: 101 KB - Last synced at: about 2 years ago - Pushed at: almost 4 years ago - Stars: 2 - Forks: 0

vincenzomartello/ERExplain

Tool to explain Entity Resolution model predictions

Language: Python - Size: 46.9 KB - Last synced at: 26 days ago - Pushed at: almost 5 years ago - Stars: 2 - Forks: 0

helenzhao093/interactive-feature-selection

Interactive feature selection web application

Language: JavaScript - Size: 10.9 MB - Last synced at: almost 2 years ago - Pushed at: over 5 years ago - Stars: 2 - Forks: 0

boyanangelov/sdmexplain

Explainable Species Distribution Modeling

Language: R - Size: 1.25 MB - Last synced at: over 1 year ago - Pushed at: over 5 years ago - Stars: 2 - Forks: 0

JohnNay/sa

Sensitivity Analysis for Understanding Complex Computational Models

Language: R - Size: 49.8 KB - Last synced at: over 2 years ago - Pushed at: about 9 years ago - Stars: 2 - Forks: 0

maxreiss123/GeneExpressionProgramming.jl

Gene Expression Programming for symbolic regression in Julia

Language: Julia - Size: 3.63 MB - Last synced at: 18 days ago - Pushed at: 18 days ago - Stars: 1 - Forks: 1

ritu-thombre99/explaining_quanvolution

This work explores whether the quanvolution neural network is explainable by proposing a novel mathematical approach for quantifying explainability

Language: Jupyter Notebook - Size: 844 MB - Last synced at: 3 days ago - Pushed at: 23 days ago - Stars: 1 - Forks: 0

bgreenwell/SampleSHAP.jl

A Julia port of the fastshap package in R

Language: Julia - Size: 305 KB - Last synced at: 27 days ago - Pushed at: 28 days ago - Stars: 1 - Forks: 0

mmaisonnave/unplanned-hospital-readmission-prediction

Explainable ML applied to healthcare data from Nova Scotia (Canada) to identify patients at risk of unplanned hospital readmission.

Language: Python - Size: 532 MB - Last synced at: 5 months ago - Pushed at: 5 months ago - Stars: 1 - Forks: 0

LukePower01/ml-to-qml

Final year project, exploring the field of quantum machine learning.

Language: Jupyter Notebook - Size: 166 MB - Last synced at: 6 months ago - Pushed at: 6 months ago - Stars: 1 - Forks: 1

Pranav2092/Intrustion-Detection-Using-Modified-Tree-SHAP

Language: Jupyter Notebook - Size: 118 KB - Last synced at: 3 months ago - Pushed at: 8 months ago - Stars: 1 - Forks: 0

NREL/BUTTER-Clarifier

This repository contains a python package of neural network interpretability and explainablility methods, focusing on the latent space, that can be easily integrated into a keras training routine using a callback to compute and capture outputs of these methods during training.

Language: Python - Size: 15.6 KB - Last synced at: 6 months ago - Pushed at: 10 months ago - Stars: 1 - Forks: 0

gesiscss/Discrimination-in-Relational-Classification

Data-driven discrimination in relational classification

Language: Jupyter Notebook - Size: 253 MB - Last synced at: 3 months ago - Pushed at: 11 months ago - Stars: 1 - Forks: 1

OmidGhadami95/EfficientNetV2_CatVSDog

Binary classification, SHAP (Explainable Artificial Intelligence), and Grid Search (for tuning hyperparameters) using EfficientNetV2-B0 on Cat VS Dog dataset.

Language: Jupyter Notebook - Size: 1.54 MB - Last synced at: 4 months ago - Pushed at: about 1 year ago - Stars: 1 - Forks: 0