An R package for computing asymmetric Shapley values to assess causality in any trained machine learning model
-
Updated
Jun 9, 2020 - R
An R package for computing asymmetric Shapley values to assess causality in any trained machine learning model
Deep Treatment Learning (R)
Weighted Shapley Values and Weighted Confidence Intervals for Multiple Machine Learning Models and Stacked Ensembles
Customer Churn Analysis in R: Logistic, Classification Tree, XGBoost, Random Forest.
Predicting bank term deposits using classification ML algorithms.
Modelling customers' behaviour for better marketing strategies. Constructing the baseline behavioural scorecard model to fasten the mortgage application process. In cooperation with Atom Bank.
An exploratory analysis of Chicago community areas.
predictive model to output a list of features that influence whether or not a searching customer decides to purchase a product
Results of binary classification of Yelp reviews as pertaining to conventional or alternative medicine using random forests
Add a description, image, and links to the feature-importance topic page so that developers can more easily learn about it.
To associate your repository with the feature-importance topic, visit your repo's landing page and select "manage topics."