Author Mark Steadman
Data Science Engineering Architect
As Data Science Engineering Architect at DataRobot, Mark designs and builds key components of automated machine learning infrastructure. He contributes both by leading large cross-functional project teams and tackling challenging data science problems. Before working at DataRobot and data science he was a physicist where he did data analysis and detector work for the Olympus experiment at MIT and DESY.
Posts by Mark Steadman
Kaggle competitions are a great means of sharpening your data science skills by getting exposed to challenging problems and a variety of domains and datasets. At DataRobot we use Kaggle competitions not only to hone our skills, but also to test out the platform we’re building. We’re excited to announce that team DataRobot placed 2nd out of 473 teams in…
Gradient Boosted Regression Trees (GBRT) or shorter Gradient Boosting is a flexible non-parametric statistical learning technique for classification and regression. This notebook shows how to use GBRT in scikit-learn, an easy-to-use, general-purpose toolbox for machine learning in Python. We will start by giving a brief introduction to scikit-learn and its GBRT interface. The bulk of the tutorial will show how…
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This short tutorial will not only guide you through some basic data analysis methods but it will also show you how to implement some of the more sophisticated techniques available today. We will look into traffic accident data from the National Highway Traffic Safety Administration and try to predict fatal accidents using state-of-the-art statistical learning techniques. If you are interested,…