Feature Engineering with H2O - Dmitry Larko, Senior Data Scientist, H2O.ai

Name: Feature Engineering with H2O - Dmitry Larko, Senior Data Scientist, H2O.ai
Uploaded: 2017-12-06
Duration: 59 min
Description: H2O's Dmitry Larko gives a practical tour of feature engineering: what it is versus data cleaning, and the encodings that make a great tabular model.

Predictive AI
H2O Feature Store

Overview

H2O senior data scientist and Kaggle competitor Dmitry Larko gives a deep, practical tour of feature engineering: what counts as feature engineering versus data cleaning, and the techniques that separate a good tabular model from a great one. He covers categorical encoding methods from one-hot to target mean encoding with smoothing, numerical transformations, feature extraction from GPS and datetime fields, and answers extensive audience questions on validation, missing values, and time series pitfalls.

Key takeaways

The difference between feature engineering and data cleaning or scaling, and why target and business context should drive most feature choices
Categorical encoding options and their tradeoffs: one-hot, label, frequency, target mean encoding with smoothing for rare categories, leave-one-out, and weight of evidence
How to use information value as a rule of thumb for judging a feature's predictive power, and as a warning sign for data leakage
Numerical feature techniques: quantile binning, dimensionality reduction such as PCA or SVD, clustering, and distance-to-centroid features
How to extract new features from raw fields, such as mapping GPS coordinates to zip codes or pulling year, month, day, and holiday flags from timestamps

Start owning your AI

One platform. Complete sovereignty. Measurable results.

Request a demo Talk to H2O