SOTAVerified

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 1501–1510 of 1706 papers

TitleStatusHype
Borrow a Little from your Rich Cousin: Using Embeddings and Polarities of English Words for Multilingual Sentiment Classification—0
Efficient Learning of Control Policies for Robust Quadruped Bounding using Pretrained Neural Networks—0
Breast mass classification in ultrasound based on Kendall's shape manifold—0
Bridging the Semantic Gap in Virtual Machine Introspection and Forensic Memory Analysis—0
Bringing Structure to Naturalness: On the Naturalness of ASTs—0
BUCC 2017 Shared Task: a First Attempt Toward a Deep Learning Framework for Identifying Parallel Sentences in Comparable Corpora—0
Building automated vandalism detection tools for Wikidata—0
Building Trainable Taggers in a Web-based, UIMA-Supported NLP Workbench—0
C1 at SemEval-2020 Task 9: SentiMix: Sentiment Analysis for Code-Mixed Social Media Text using Feature Engineering—0
Cached Long Short-Term Memory Neural Networks for Document-Level Sentiment Classification—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified