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 1221–1230 of 1706 papers

TitleStatusHype
Learning Non-linear Features for Machine Translation Using Gradient Boosting Machines—0
Learning Orthographic Features in Bi-directional LSTM for Biomedical Named Entity Recognition—0
Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction—0
Learning post-processing for QRS detection using Recurrent Neural Network—0
Learning Representations from Road Network for End-to-End Urban Growth Simulation—0
Learning Semantic Textual Similarity with Structural Representations—0
Learning Stylometric Representations for Authorship Analysis—0
Learning Summary Prior Representation for Extractive Summarization—0
Learning Through Guidance: Knowledge Distillation for Endoscopic Image Classification—0
Learning to Extract Coherent Summary via Deep Reinforcement Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified