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Feature Engineering

Feature engineering is the process of taking a dataset and constructing explanatory variables — features — that can be used to train a machine learning model for a prediction problem. Often, data is spread across multiple tables and must be gathered into a single table with rows containing the observations and features in the columns.

The traditional approach to feature engineering is to build features one at a time using domain knowledge, a tedious, time-consuming, and error-prone process known as manual feature engineering. The code for manual feature engineering is problem-dependent and must be re-written for each new dataset.

Papers

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Showing 761–770 of 1706 papers

TitleStatusHype
Feature Engineering in Learning-to-Rank for Community Question Answering Task—0
Feature Engineering in the NLI Shared Task 2013: Charles University Submission Report—0
BUCC 2017 Shared Task: a First Attempt Toward a Deep Learning Framework for Identifying Parallel Sentences in Comparable Corpora—0
Feature Engineering on LMS Data to Optimize Student Performance Prediction—0
Feature Engineering vs BERT on Twitter Data—0
Feature engineering vs. deep learning for paper section identification: Toward applications in Chinese medical literature—0
Event Nugget Detection with Forward-Backward Recurrent Neural Networks—0
Event Extraction with Generative Adversarial Imitation Learning—0
Bringing Structure to Naturalness: On the Naturalness of ASTs—0
AnyThreat: An Opportunistic Knowledge Discovery Approach to Insider Threat Detection—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified