Topic: "shap"
shap/shap
A game theoretic approach to explain the output of any machine learning model.
Language: Jupyter Notebook - Size: 301 MB - Last synced at: 6 days ago - Pushed at: 6 days ago - Stars: 23,722 - Forks: 3,354
MAIF/shapash
🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
Language: Jupyter Notebook - Size: 61.8 MB - Last synced at: 13 days ago - Pushed at: about 1 month ago - Stars: 2,863 - Forks: 345
oegedijk/explainerdashboard
Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.
Language: Python - Size: 80.2 MB - Last synced at: 6 months ago - Pushed at: 9 months ago - Stars: 2,303 - Forks: 332
cerlymarco/shap-hypetune
A python package for simultaneous Hyperparameters Tuning and Features Selection for Gradient Boosting Models.
Language: Jupyter Notebook - Size: 122 KB - Last synced at: 14 days ago - Pushed at: 11 months ago - Stars: 577 - Forks: 73
linkedin/FastTreeSHAP
Fast SHAP value computation for interpreting tree-based models
Language: Python - Size: 152 MB - Last synced at: 12 days ago - Pushed at: almost 2 years ago - Stars: 537 - Forks: 34
mmschlk/shapiq
Shapley Interactions and Shapley Values for Machine Learning
Language: Python - Size: 307 MB - Last synced at: 8 days ago - Pushed at: 10 days ago - Stars: 504 - Forks: 34
jiangnanboy/learning_to_rank
利用lightgbm做(learning to rank)排序学习，包括数据处理、模型训练、模型决策可视化、模型可解释性以及预测等。Use LightGBM to learn ranking, including data processing, model training, model decision visualization, model interpretability and prediction, etc.
Language: Python - Size: 2 MB - Last synced at: 29 days ago - Pushed at: over 2 years ago - Stars: 265 - Forks: 71
predict-idlab/powershap
A power-full Shapley feature selection method.
Language: Python - Size: 4.64 MB - Last synced at: 6 days ago - Pushed at: 12 months ago - Stars: 204 - Forks: 20
feedzai/timeshap
TimeSHAP explains Recurrent Neural Network predictions.
Language: Jupyter Notebook - Size: 1.53 MB - Last synced at: 16 days ago - Pushed at: over 1 year ago - Stars: 171 - Forks: 32
tvdboom/ATOM
Automated Tool for Optimized Modelling
Language: HTML - Size: 826 MB - Last synced at: 15 days ago - Pushed at: 9 months ago - Stars: 157 - Forks: 14
AstraZeneca/awesome-shapley-value
Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)
Size: 622 KB - Last synced at: 12 days ago - Pushed at: over 2 years ago - Stars: 148 - Forks: 12
ing-bank/probatus
SHAP-based validation for linear and tree-based models. Applied to binary, multiclass and regression problems.
Language: Python - Size: 16.5 MB - Last synced at: 6 days ago - Pushed at: 6 days ago - Stars: 137 - Forks: 40
ModelOriented/survex
Explainable Machine Learning in Survival Analysis
Language: R - Size: 309 MB - Last synced at: 7 days ago - Pushed at: 10 months ago - Stars: 111 - Forks: 10
ModelOriented/shapviz
SHAP Plots in R
Language: R - Size: 41.1 MB - Last synced at: 15 days ago - Pushed at: 15 days ago - Stars: 90 - Forks: 14
nredell/ShapML.jl
A Julia package for interpretable machine learning with stochastic Shapley values
Language: Julia - Size: 529 KB - Last synced at: 11 days ago - Pushed at: 12 months ago - Stars: 90 - Forks: 8
snehankekre/streamlit-shap
streamlit-shap provides a wrapper to display SHAP plots in Streamlit.
Language: Python - Size: 4.52 MB - Last synced at: 13 days ago - Pushed at: almost 3 years ago - Stars: 85 - Forks: 9
MI2DataLab/survshap
SurvSHAP(t): Time-dependent explanations of machine learning survival models
Language: Jupyter Notebook - Size: 8.99 MB - Last synced at: 28 days ago - Pushed at: over 1 year ago - Stars: 84 - Forks: 16
ModelOriented/treeshap
Compute SHAP values for your tree-based models using the TreeSHAP algorithm
Language: R - Size: 19.7 MB - Last synced at: 25 days ago - Pushed at: 9 months ago - Stars: 83 - Forks: 24
nredell/shapFlex
An R package for computing asymmetric Shapley values to assess causality in any trained machine learning model
Language: R - Size: 2.13 MB - Last synced at: 8 days ago - Pushed at: almost 5 years ago - Stars: 74 - Forks: 7
dylan-slack/Fooling-LIME-SHAP
Adversarial Attacks on Post Hoc Explanation Techniques (LIME/SHAP)
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xplainable/xplainable
Real-time explainable machine learning for business optimisation
Language: Python - Size: 19.4 MB - Last synced at: 7 months ago - Pushed at: 7 months ago - Stars: 57 - Forks: 6
AidanCooper/shap-analysis-guide
How to Interpret SHAP Analyses: A Non-Technical Guide
Language: Jupyter Notebook - Size: 7.32 MB - Last synced at: 5 months ago - Pushed at: over 3 years ago - Stars: 45 - Forks: 8
TannerGilbert/Model-Interpretation
Overview of different model interpretability libraries.
Language: Jupyter Notebook - Size: 19.8 MB - Last synced at: 12 months ago - Pushed at: almost 3 years ago - Stars: 38 - Forks: 13
ModelOriented/kernelshap
Efficient R implementation of SHAP
Language: R - Size: 2.36 MB - Last synced at: 12 months ago - Pushed at: about 1 year ago - Stars: 30 - Forks: 7
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: 16 days ago - Pushed at: almost 3 years ago - Stars: 30 - Forks: 5
dylan-slack/Modeling-Uncertainty-Local-Explainability
Local explanations with uncertainty 💐!
Language: Python - Size: 7.57 MB - Last synced at: about 2 years ago - Pushed at: over 2 years ago - Stars: 26 - Forks: 9
cloudera/CML_AMP_Explainability_LIME_SHAP
Learn how to explain ML models using LIME and SHAP.
Language: Jupyter Notebook - Size: 4.65 MB - Last synced at: 8 days ago - Pushed at: over 1 year ago - Stars: 23 - Forks: 11
ds-wook/ai-hackathon
🏆데이콘 AI해커톤 대회 우수상 솔루션🏆
Language: Python - Size: 66.4 KB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 18 - Forks: 0
VishalKumar-S/Sales_Conversion_Optimization_MLOps_Project
Sales Conversion Optimization MLOps: Boost revenue with AI-powered insights. Features H2O AutoML, ZenML pipelines, Neptune.ai tracking, data validation, drift analysis, CI/CD, Streamlit app, Docker, and GitHub Actions. Includes e-mail alerts, Discord/Slack integration, and SHAP interpretability. Streamline ML workflow and enhance sales performance.
Language: HTML - Size: 13.5 MB - Last synced at: about 1 month ago - Pushed at: about 1 month ago - Stars: 17 - Forks: 3
hi-paris/XPER
A methodology designed to measure the contribution of the features to the predictive performance of any econometric or machine learning model.
Language: Python - Size: 8.26 MB - Last synced at: 13 days ago - Pushed at: 5 months ago - Stars: 16 - Forks: 0
PERSIMUNE/explainer
ExplaineR is an R package built for enhanced interpretation of classification and regression models based on SHAP method and interactive visualizations with unique functionalities so please feel free to check it out, See ExplaineR paper at doi:10.1093/bioadv/vbae049
Language: R - Size: 17.8 MB - Last synced at: 1 day ago - Pushed at: 7 months ago - Stars: 16 - Forks: 1
GeoAIR-lab/XAI-tool4GEE
A Colab notebook for land cover mapping and monitoring using Earth Engine
Language: Jupyter Notebook - Size: 114 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 16 - Forks: 7
akthammomani/Menara-App-Predict-House-Price-CA
Build a Web App called Menara to Predict, Forecast House Prices and search GreatSchools in California - Bay Area
Language: Jupyter Notebook - Size: 91.1 MB - Last synced at: 5 months ago - Pushed at: about 1 year ago - Stars: 15 - Forks: 2
epfl-ml4ed/evaluating-explainers
Comparing 5 different XAI techniques (LIME, PermSHAP, KernelSHAP, DiCE, CEM) through quantitative metrics. Published at EDM 2022.
Language: PureBasic - Size: 6.15 MB - Last synced at: 6 months ago - Pushed at: over 2 years ago - Stars: 15 - Forks: 2
rodrigobressan/keras_boston_housing_price
Keras 101: A simple Neural Network for House Pricing regression
Language: Jupyter Notebook - Size: 1.62 MB - Last synced at: 14 days ago - Pushed at: over 5 years ago - Stars: 15 - Forks: 5
JK-Future-GitHub/NBA_Champion
I will predict the 2023 NBA Champion using Machine Learning
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Raman-Lab-UCLA/Explainability_for_Photonics
Here, we use Deep SHAP (or SHAP) to explain the behavior of nanophotonic structures learned by a convolutional neural network (CNN). Reference: https://pubs.acs.org/doi/full/10.1021/acsphotonics.0c01067
Language: Python - Size: 7.05 MB - Last synced at: almost 2 years ago - Pushed at: almost 3 years ago - Stars: 14 - Forks: 1
PrashantSaikia/Dynamic-SHAP-Plots
Enabling interactive plotting of the visualizations from the SHAP project.
Language: Python - Size: 40 KB - Last synced at: almost 2 years ago - Pushed at: over 5 years ago - Stars: 14 - Forks: 3
harshjuly12/Enhancing-Explainability-in-Fake-News-Detection-A-SHAP-Based-Approach-for-Bidirectional-LSTM-Models
Enhancing Explainability in Fake News Detection uses SHAP and BiLSTM models to improve the transparency and interpretability of detecting fake news, providing insights into the model's decision-making process.
Language: Jupyter Notebook - Size: 199 KB - Last synced at: 19 days ago - Pushed at: 6 months ago - Stars: 12 - Forks: 3
chaitjo/working-women
Code for the paper 'Working Women and Caste in India' (ICLR 2019 AI for Social Good Workshop)
Language: Jupyter Notebook - Size: 212 MB - Last synced at: about 2 years ago - Pushed at: over 2 years ago - Stars: 12 - Forks: 2
hiroki-kawauchi/SHAPObjectDetection
SHAP-Based Interpretable Object Detection Method for Satellite Imagery
Language: Python - Size: 3.25 MB - Last synced at: about 1 year ago - Pushed at: over 2 years ago - Stars: 12 - Forks: 1
ata-turhan/Titanic-Survival-Prediction
A comprehensive solution for the Kaggle Titanic Challenge, featuring advanced data exploration, feature engineering, model training, and explainable AI techniques. Includes Logistic Regression, RandomForest, XGBoost, and Stacked Ensembles with SHAP and permutation importance for model interpretability.
Language: Jupyter Notebook - Size: 1.75 MB - Last synced at: 12 days ago - Pushed at: 5 months ago - Stars: 11 - Forks: 0
alexcoca/DistributedKernelShap
Language: Jupyter Notebook - Size: 2.49 MB - Last synced at: 12 months ago - Pushed at: over 4 years ago - Stars: 11 - Forks: 2
ckorgial/xAI-CAAE
Pytorch Implementation of the Explainable Conditional Adversarial Autoencoder using Saliency Maps and SHAP (J. of Imaging - MDPI)
Language: Python - Size: 156 KB - Last synced at: about 2 months ago - Pushed at: about 2 months ago - Stars: 10 - Forks: 2
haghish/shapley
Weighted Shapley Values and Weighted Confidence Intervals for Multiple Machine Learning Models and Stacked Ensembles
Language: R - Size: 2.88 MB - Last synced at: 11 months ago - Pushed at: 11 months ago - Stars: 10 - Forks: 0
erik1110/Data-Science
iThome 13th-ironman (2021) - Data Science Learning Roadmap about Python
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MayurDivate/DeepCancerSignatures
This repository contains code used to build and interpret a deep learning model. It is a DNN classifier trained using gene expression data (TCGA). Then is interpreted to identify cancer specific gene expression signatures.
Language: Jupyter Notebook - Size: 688 KB - Last synced at: 13 days ago - Pushed at: almost 4 years ago - Stars: 9 - Forks: 3
tsitsimis/tinyshap
Python package providing a minimal implementation of the SHAP algorithm using the Kernel method
Language: Jupyter Notebook - Size: 177 KB - Last synced at: 15 days ago - Pushed at: almost 2 years ago - Stars: 8 - Forks: 1
BBloggsbott/masters-chance-of-admit
A website that provides analytics on how different features contribute to your chances of getting into a university of your choice.
Language: HTML - Size: 597 KB - Last synced at: about 2 years ago - Pushed at: over 2 years ago - Stars: 8 - Forks: 20
tsurubee/shappack
Interpretable machine learning based on Shapley values
Language: Python - Size: 473 KB - Last synced at: over 1 year ago - Pushed at: almost 4 years ago - Stars: 8 - Forks: 0
FernandoLpz/SHAP-Classification-example
This repository contains an example of how to implement the shap library to interpret a machine learning model.
Language: Jupyter Notebook - Size: 171 KB - Last synced at: about 2 years ago - Pushed at: almost 4 years ago - Stars: 8 - Forks: 2
miolab/jupyterlab_poetry
JupyterLab runtime environment with Poetry and Docker management.
Language: Jupyter Notebook - Size: 108 MB - Last synced at: 5 days ago - Pushed at: 5 days ago - Stars: 7 - Forks: 0
hbaniecki/compress-then-explain
Efficient and accurate explanation estimation with distribution compression (ICLR 2025 Spotlight)
Language: Python - Size: 1.25 MB - Last synced at: 10 days ago - Pushed at: 2 months ago - Stars: 7 - Forks: 1
AliAmini93/Telecom-Churn-Analysis
Developed a churn prediction model using XGBoost, with comprehensive data preprocessing and hyperparameter tuning. Applied SHAP for feature importance analysis, leading to actionable business insights for targeted customer retention.
Language: Jupyter Notebook - Size: 2.52 MB - Last synced at: 8 months ago - Pushed at: 8 months ago - Stars: 7 - Forks: 0
EricKenjiLee/WaveMAP_Paper
This repo allows for the complete reproduction, from processed data, of all the main and supplemental figures in the manuscript Non-linear Dimensionality Reduction on Extracellular Waveforms Reveals Physiological, Functional, and Laminar Diversity in Premotor Cortex.
Language: Jupyter Notebook - Size: 12.4 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 7 - Forks: 2
LamineTourelab/Explainable-AI
In this repository you will fine explainability of machine learning models.
Size: 8.79 KB - Last synced at: about 2 months ago - Pushed at: about 2 years ago - Stars: 7 - Forks: 0
neZorinEgor/AdsAnalyzer
📰 Platform for analyzing the effectiveness of advertising campaigns by ml and data analys
Language: Jupyter Notebook - Size: 14.2 MB - Last synced at: 4 days ago - Pushed at: 5 days ago - Stars: 6 - Forks: 0
kahramankostas/IoTGeM
IoT Attack Detection with machine learning
Language: Jupyter Notebook - Size: 78.7 MB - Last synced at: 10 months ago - Pushed at: 10 months ago - Stars: 6 - Forks: 3
Montimage/maip
Montimage AI Platform (MAIP) provides users with easy access to AI services developed by Montimage, through a friendly and intuitive interface.
Language: PureBasic - Size: 179 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 6 - Forks: 2
rezacsedu/OncoNetExplainer
OncoNetExplainer: Explainable Prediction of Cancer Types Based on Gene Expression Data
Language: Jupyter Notebook - Size: 5.09 MB - Last synced at: about 2 years ago - Pushed at: about 2 years ago - Stars: 6 - Forks: 4
balajissp/dash-shap-components
Language: Python - Size: 2.23 MB - Last synced at: almost 2 years ago - Pushed at: about 3 years ago - Stars: 6 - Forks: 1
wuhanstudio/interpretable-ml-covid-19
Interpretable Machine Learning for COVID-19
Language: Jupyter Notebook - Size: 8.95 MB - Last synced at: about 2 years ago - Pushed at: over 3 years ago - Stars: 6 - Forks: 5
IBMDeveloperUK/AIX360-Introduction
Introduction to explaining data and machine learning models with aif360
Language: Jupyter Notebook - Size: 3.34 MB - Last synced at: about 2 years ago - Pushed at: over 4 years ago - Stars: 6 - Forks: 4
jpmorganchase/cf-shap
Counterfactual SHAP: a framework for counterfactual feature importance
Language: HTML - Size: 713 KB - Last synced at: about 2 years ago - Pushed at: over 2 years ago - Stars: 5 - Forks: 2
sonnguyen129/Accident-Severity-Prediction
Predicting the severity of accident
Language: Jupyter Notebook - Size: 25.3 MB - Last synced at: over 1 year ago - Pushed at: over 2 years ago - Stars: 5 - Forks: 0
EnbinYang/tb_prediction_files
A multivariate multi-step LSTM forecasting model for tuberculosis incidence with model explanation in Liaoning Province, China
Language: Python - Size: 233 KB - Last synced at: almost 2 years ago - Pushed at: almost 3 years ago - Stars: 5 - Forks: 1
McGill-MMA-EnterpriseAnalytics/datasectuals
Predicting whether or not a person deposits money after a marketing campaign. Gain insights to develop the best strategy in the next marketing campaign
Language: Jupyter Notebook - Size: 12 MB - Last synced at: 11 months ago - Pushed at: about 5 years ago - Stars: 5 - Forks: 3
basics-lab/spectral-explain
Fast XAI with interactions at large scale. SPEX can help you understand the output of your LLM, even if you have a long context!
Language: Jupyter Notebook - Size: 4.73 MB - Last synced at: 26 days ago - Pushed at: 26 days ago - Stars: 4 - Forks: 0
akthammomani/AI_powered_heart_disease_risk_assessment_app
Build a Web App called AI-Powered Heart Disease Risk Assessment App
Language: Jupyter Notebook - Size: 31.1 MB - Last synced at: 7 months ago - Pushed at: 7 months ago - Stars: 4 - Forks: 0
pyladiesams/ai-in-finance-python-lecture-beginner-may2022
AI in Finance - Python interactive lecture for students studying Finance
Language: Jupyter Notebook - Size: 1.88 MB - Last synced at: 1 day ago - Pushed at: almost 2 years ago - Stars: 4 - Forks: 3
fpozoc/trifid
Machine Learning-based tool to assess the functional relevance of splice isoforms.
Language: Python - Size: 2.05 MB - Last synced at: about 2 years ago - Pushed at: about 2 years ago - Stars: 4 - Forks: 0
srmatth/mshap
Implementation of the mSHAP algorithm for explaining two-part models, as described by Matthews and Hartman (2021).
Language: R - Size: 96.9 MB - Last synced at: over 1 year ago - Pushed at: over 3 years ago - Stars: 4 - Forks: 0
TmLev/interpretable-ml 📦
Article on the interpretability of ML models
Language: Python - Size: 2.7 MB - Last synced at: about 22 hours ago - Pushed at: about 4 years ago - Stars: 4 - Forks: 2
wyattowalsh/higher-education-simulation
Full Python implementation of an agent-based simulation model of generalized higher education institutions. Thousands of experiments are conducted and model feature significance is found through regression, SHAP, and permutation.
Language: Jupyter Notebook - Size: 49.8 MB - Last synced at: 12 months ago - Pushed at: about 4 years ago - Stars: 4 - Forks: 0
asgaardlab/done-20-markos-dota2_win_prediction-code
Project to investigate win prediction models for Dota 2 and factors that explain such predictions
Language: Jupyter Notebook - Size: 20.9 MB - Last synced at: about 2 years ago - Pushed at: over 4 years ago - Stars: 4 - Forks: 0
Daniel-Andarge/AiML-financial-fraud-detection-model
The Fraud Detection project aims to improve identification of fraudulent activities in e-commerce and banking by developing advanced machine learning models that analyze transaction data, employ feature engineering, and implement real-time monitoring for high accuracy fraud detection.
Language: Jupyter Notebook - Size: 7.7 MB - Last synced at: 19 days ago - Pushed at: 19 days ago - Stars: 3 - Forks: 2
DeepInMotion/ShapGCN
Explaining Human Movement with SHAP
Language: Python - Size: 51.2 MB - Last synced at: about 2 months ago - Pushed at: about 2 months ago - Stars: 3 - Forks: 0
dcaup/app
Unified Pipeline with Crossmodal Data and Decentralized Agents for Causal Analysis of Financial Decision-Making Dynamics
Size: 12.7 KB - Last synced at: about 2 months ago - Pushed at: about 2 months ago - Stars: 3 - Forks: 0
cfmzk/app
Zero-Knowledge Proofs Integrated with Crossmodal and Foundational Models for Causal Analysis of Crypto Market Performance
Size: 11.7 KB - Last synced at: about 2 months ago - Pushed at: about 2 months ago - Stars: 3 - Forks: 0
serkd/app
Comparative Causal Network Analysis of Alpha Waves and HRV (Normalized) Using Knowledge Retrieval for Emotion Recognition Systems
Size: 10.7 KB - Last synced at: about 2 months ago - Pushed at: about 2 months ago - Stars: 3 - Forks: 0
PragyanTiwari/Breast-Cancer-Prediction-with-DecisionTree-Classifier
DecisionTree Classifier to predict breast cancer. Tuning model with feature engineering techniques and interpreting model behaviour with SHAP.
Language: Jupyter Notebook - Size: 3.12 MB - Last synced at: 2 months ago - Pushed at: 2 months ago - Stars: 3 - Forks: 0
pavankethavath/Microsoft-Classifying-Cybersecurity-Incidents-with-ML
A machine learning pipeline for classifying cybersecurity incidents as True Positive(TP), Benign Positive(BP), or False Positive(FP) using the Microsoft GUIDE dataset. Features advanced preprocessing, XGBoost optimization, SMOTE, SHAP analysis, and deployment-ready models. Tools: Python, scikit-learn, XGBoost, LightGBM, SHAP and imbalanced-learn
Language: Jupyter Notebook - Size: 4.54 MB - Last synced at: 23 days ago - Pushed at: 5 months ago - Stars: 3 - Forks: 0
brickmanlab/scanvi-explainer
scANVI Explainer
Language: Python - Size: 2.64 MB - Last synced at: 3 days ago - Pushed at: 7 months ago - Stars: 3 - Forks: 0
Xinbingru/COFsMembraneML
A machine learning implementation of an interpretable model for membrane separation performance prediction of COFs materials.
Language: Jupyter Notebook - Size: 8.53 MB - Last synced at: 11 months ago - Pushed at: 11 months ago - Stars: 3 - Forks: 0
carlacodes/boostmodels
gradient-boosted regression and decision tree models on behavioural animal data
Language: Python - Size: 1.24 GB - Last synced at: 11 months ago - Pushed at: 11 months ago - Stars: 3 - Forks: 0
jpmorganchase/cf-shap-facct22
Counterfactual Shapley Additive Explanation: Experiments
Language: Jupyter Notebook - Size: 1.33 MB - Last synced at: about 2 years ago - Pushed at: about 2 years ago - Stars: 3 - Forks: 3
abhimanyubhowmik/DBNex
A repository for the research article titled "DBNex: Deep Belief Network and Explainable AI based Financial Fraud Detection".
Language: Jupyter Notebook - Size: 47.7 MB - Last synced at: almost 2 years ago - Pushed at: about 2 years ago - Stars: 3 - Forks: 1
gulabpatel/ExplainableAI
Language: Jupyter Notebook - Size: 6.06 MB - Last synced at: almost 2 years ago - Pushed at: over 2 years ago - Stars: 3 - Forks: 0
tommartensen/tic
TIC is a library that acts as a Toolbox for Interpretability Comparison.
Language: Python - Size: 212 KB - Last synced at: 1 day ago - Pushed at: over 2 years ago - Stars: 3 - Forks: 0
aliciapj/xai-genz
Explainable AI & fashion talk & experiments
Language: Jupyter Notebook - Size: 55.6 MB - Last synced at: about 2 years ago - Pushed at: over 2 years ago - Stars: 3 - Forks: 0
alpankratov/NY_state_schools_dropout_rate_prediction
This project aims to build and compare four different models predicting the dropout rates in schools in New York state as well as to understand why models make a certain prediction (see PDF file with the memo for details)
Language: Python - Size: 22.9 MB - Last synced at: about 1 year ago - Pushed at: over 2 years ago - Stars: 3 - Forks: 0
josedv82/NBA_Schedule_XGBoost_Classifier
Predicting NBA game outcomes using schedule related information. This is an example of supervised learning where a xgboost model was trained with 20 seasons worth of NBA games and uses SHAP values for model explainability.
Language: Jupyter Notebook - Size: 12.9 MB - Last synced at: 23 days ago - Pushed at: over 3 years ago - Stars: 3 - Forks: 0
KatyaZeross/TrekPredict
An NLP analysis on the impact of Star Trek: The Next Generation's character spoken lines and how it affects the rating of the episode.
Language: Jupyter Notebook - Size: 83.8 MB - Last synced at: 5 months ago - Pushed at: almost 4 years ago - Stars: 3 - Forks: 0
gianluigilopardo/HELOC-Credit-Approval
This notebook is ispired by the AIX360 HELOC Credit Approval Tutorial, which shows different explainability methods for a credit approval process. Here XGBoost is used for classification, achieving better accuracy than most of the models used in that notebook. Then, feature importance methods are shown, to be compared with the Data Scientist explanations methods provided in the above notebook. The first ones come directly with XGBoost and the other is based on SHAP.
Language: Jupyter Notebook - Size: 781 KB - Last synced at: almost 2 years ago - Pushed at: almost 4 years ago - Stars: 3 - Forks: 0
SmellyArmure/OC_DS_Project7
Implémentation d'un modèle de scoring (OpenClassrooms | Data Scientist | Projet 7)
Language: Jupyter Notebook - Size: 34.7 MB - Last synced at: about 2 years ago - Pushed at: about 4 years ago - Stars: 3 - Forks: 2
asgaardlab/dota2-prediction-models
Repository with code for building, evaluating and explaining Dota 2 prediction models for team victory. Submitted to the artifact evaluation track of the 16th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment - AIIDE 2020
Language: Jupyter Notebook - Size: 428 KB - Last synced at: about 2 years ago - Pushed at: over 4 years ago - Stars: 3 - Forks: 0
chengning-zhang/Simple-TAN-and-Ensemble-TAN
Bayesian network implementation API inspired by SciKit-learn.
Language: Jupyter Notebook - Size: 7.24 MB - Last synced at: over 1 year ago - Pushed at: about 5 years ago - Stars: 3 - Forks: 0
SeyedMuhammadHosseinMousavi/Is-Deleting-the-Dataset-of-a-Self-Aware-AGI-ethical-Does-It-Possess-a-Soul-by-Self-Awareness-
Is Deleting the Dataset of a Self-Aware AGI ethical? Does It Possess a Soul by Self-Awareness? Assessing the Existence of a Soul and Ethical Implications Using Fuzzy Logic
Language: Python - Size: 39.1 KB - Last synced at: 15 days ago - Pushed at: 15 days ago - Stars: 2 - Forks: 1
donlelef/shap-and-emb-explain-your-gradient-boosting
Code and material for the talk "From SHAP to EBM: Explain you Gradient Boosting Model with Python"
Language: Jupyter Notebook - Size: 756 KB - Last synced at: 5 months ago - Pushed at: 5 months ago - Stars: 2 - Forks: 0
carlacodes/neuraldecoding
scripts used for neural decoding of single and multi unit auditory cortex data
Language: Python - Size: 3.37 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 2 - Forks: 1