MLFriend enumerates prediction tasks for event-driven time-series data and interactively recommends useful ones, with evaluation on three datasets yielding 2885 tasks of which 722 were deemed useful by experts.
Feature Engineering for Predictive Modeling using Reinforcement Learning
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abstract
Feature engineering is a crucial step in the process of predictive modeling. It involves the transformation of given feature space, typically using mathematical functions, with the objective of reducing the modeling error for a given target. However, there is no well-defined basis for performing effective feature engineering. It involves domain knowledge, intuition, and most of all, a lengthy process of trial and error. The human attention involved in overseeing this process significantly influences the cost of model generation. We present a new framework to automate feature engineering. It is based on performance driven exploration of a transformation graph, which systematically and compactly enumerates the space of given options. A highly efficient exploration strategy is derived through reinforcement learning on past examples.
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cs.LG 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
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MLFriend: Interactive Prediction Task Recommendation for Event-Driven Time-Series Data
MLFriend enumerates prediction tasks for event-driven time-series data and interactively recommends useful ones, with evaluation on three datasets yielding 2885 tasks of which 722 were deemed useful by experts.