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 601–610 of 1706 papers

TitleStatusHype
Combining Lexical and Semantic-based Features for Answer Sentence Selection—0
Alejandro Mosquera at SemEval-2021 Task 1: Exploring Sentence and Word Features for Lexical Complexity Prediction—0
A Deep Learning Based Cost Model for Automatic Code Optimization—0
A Conditional Generative Model for Predicting Material Microstructures from Processing Methods—0
ABM: an automatic supervised feature engineering method for loss based models based on group and fused lasso—0
Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT—0
Combination of Diverse Ranking Models for Personalized Expedia Hotel Searches—0
Argument Labeling of Explicit Discourse Relations using LSTM Neural Networks—0
Cognito: Automated Feature Engineering for Supervised Learning—0
A Review on Deep Learning Techniques Applied to Answer Selection—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified