Predict the outcome of childbirth, from a data set containing socio-economic data of the mother-to-be, and from previous Ante Natal Care checkups
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Updated
Mar 8, 2018 - Python
Predict the outcome of childbirth, from a data set containing socio-economic data of the mother-to-be, and from previous Ante Natal Care checkups
Features selector based on the self selected-algorithm, loss function and validation method
Feature importance by the permutation method (for fastai V1)
Analysis of 'Attention is not Explanation' performed for the University of Amsterdam's Fairness, Accountability, Confidentiality and Transparency in AI Course Assignment, January 2020
Conformal Inference tools using python
Bank Marketing Data Set Binary Classification in python
Using Machine learning to predict a student final grade
Using / reproducing DAC from the paper "Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees"
IN PROGRESS - after the paper "Shapley-Lorenz decompositions in eXplainable Artificial Intelligence" by Giudici and Raffinetti - 2020
This is the official source code of our IEA/AIE 2021 paper
We create a model using the gradient boosting algorithm to cut down on the noise and improve performance. This work was done during an informal project under Prof. Yaganti while studying at BITS.
Implementation of the Integrated Directional Gradients method for Deep Neural Network model explanations.
XAI - An eXplainability toolbox for machine learning
Code for the suspended paper/project "Meta-Learning Feature Importance".
Hitting vs Pitching vs Fielding vs Baserunning (Feature Importance)
In this project, I build an ML model to predict the scores of beer reviews and extract the most important features.
Generating feature importances for outliers identified through Isolation Forests
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.
Implementation of Dual Interpretable Model-agnostic Explanations for Rasa DIET classifiers
Module for measuring feature importance for any clustering method.
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