Missing Data in Supervised Machine Learning Open Data Science Conference

Editor's note: Andras is a speaker for ODSC West 2019! Datasets are almost never complete and this can introduce various biases to your analysis. Due to these biases, your supervised machine learning model can produce incorrect predictions. The goal of this post is to give you an idea of why some of the most common approaches for dealing with missing values often introduce some type of bias. At ODSC West 2019, I will describe the methods and techniques that can help you to arrive at an unbiased conclusion in the face of missing data.