A Gaussian Process model for Heteroscedasticity June 28, 2021SaremBayesian Methods, Gaussian Process, Julia, Machine Learning, Probability, Uncertainty

Why probability and uncertainty should be an integral part of regression models (II) October 9, 2020SaremJulia, Linear Model, Machine Learning, Probability, Regression, Statistics, Uncertainty

A brief, probabilistic demand forecast model May 7, 2020SaremAlgorithms, Applications, Forecasting, Julia, Machine Learning, Time Series

Generalized Additive Neural Networks December 17, 2019Sarem2 CommentsInterpretable Machine Learning, Julia, Linear Model, Machine Learning, Neural Networks

More simple time-series models – this time with Decision Trees February 24, 2019SaremApplications, Decision Trees, Forecasting, Machine Learning, Time Series

A partially interpretable, semi-parametric Neural Network April 9, 2018SaremAlgorithms, Applications, Machine Learning, Neural Networks, Python, Uncategorized

A brief proof-of-concept for Kernelized Decision Trees March 22, 2018SaremApplications, Classification, Decision Trees, Machine Learning, Python

How Decision Trees can learn non-rectangular decision splits March 18, 2018SaremClassification, Decision Trees, Machine Learning, Python

RuleFit on real-world data March 10, 2018Sarem16 CommentsAlgorithms, Applications, Decision Trees, Gradient Boosting, Machine Learning, Python

RuleFit for interpretable Machine Learning March 10, 2018Sarem8 CommentsAlgorithms, Decision Trees, Machine Learning, Python, Random Forest