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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1671–1680 of 1706 papers

TitleStatusHype
Deep Learning for Chinese Word Segmentation and POS Tagging—0
A Feature Induction Algorithm with Application to Named Entity Disambiguation—0
Dual Training and Dual Prediction for Polarity Classification—0
LFG-based Features for Noun Number and Article Grammatical Errors—0
Learning Adaptable Patterns for Passage Reranking—0
Reducing Annotation Effort for Quality Estimation via Active Learning—0
Co-regularizing character-based and word-based models for semi-supervised Chinese word segmentation—0
Parsing with Compositional Vector Grammars—0
Learning Semantic Textual Similarity with Structural Representations—0
The Haves and the Have-Nots: Leveraging Unlabelled Corpora for Sentiment Analysis—0
Show:102550
← PrevPage 168 of 171Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified