A methodology designed to measure the contribution of the features to the predictive performance of any econometric or machine learning model.
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Updated
Jun 4, 2024 - Python
A methodology designed to measure the contribution of the features to the predictive performance of any econometric or machine learning model.
Accurate prediction of CRISPR-Cas9 off-target activity by learning to utilize internal protein 3D nanoenvironment descriptors
scripts used for neural decoding of single and multi unit auditory cortex data
Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
ImputeVIS - eXascale Infolab, University of Fribourg, Switzerland
gradient-boosted regression and decision tree models on behavioural animal data
Real-time explainable machine learning for business optimisation
A power-full Shapley feature selection method.
Validation (like Recursive Feature Elimination for SHAP) of (multiclass) classifiers & regressors and data used to develop them.
Workflow to identify functional cis-regulatory regions for each annotated cell type
ExplainableFL is a PIP Python package designed to bring explainability to Federated Learning models using SHAP values. It provides easy-to-use methods to visualize the impact of model features and privacy mechanisms on model performance.
House-Price-Prediction-App
Pytorch Implementation of the Explainable Conditional Adversarial Autoencoder using Saliency Maps and SHAP (J. of Imaging - MDPI)
Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.
🏆데이콘 AI해커톤 대회 우수상 솔루션🏆
A set of custom python modules for friendly workflow on pandas
Webapp/Application implemention of my thesis about XAI and Interpretability of Transformer Models.
The primary goal of this project is to convert free users of a financial tracking app into paid members. This conversion will be achieved by building a model that identifies users who are unlikely to enroll in the paid version of the app.
Low-code machine learning and deep learning
An explainability model that can be applied to BERT-based Turkish sentiment analysis models has been developed and its performance has been compared with model spesific Layer-wise relevance propogation expailanbility model of Hila Chefer.
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