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 1671–1680 of 1706 papers

TitleStatusHype
Detection of Product Comparisons - How Far Does an Out-of-the-Box Semantic Role Labeling System Take You?—0
A Feature Induction Algorithm with Application to Named Entity Disambiguation—0
Investigation of annotator's behaviour using eye-tracking data—0
LFG-based Features for Noun Number and Article Grammatical Errors—0
Learning Adaptable Patterns for Passage Reranking—0
Learning Non-linear Features for Machine Translation Using Gradient Boosting Machines—0
Additive Neural Networks for Statistical Machine Translation—0
Reducing Annotation Effort for Quality Estimation via Active Learning—0
Learning Semantic Textual Similarity with Structural Representations—0
Co-regularizing character-based and word-based models for semi-supervised Chinese word segmentation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified