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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 1411–1420 of 1706 papers

TitleStatusHype
Joint RNN Model for Argument Component Boundary DetectionCode0
On the effectiveness of feature set augmentation using clusters of word embeddings—0
Optimizing a PoS Tagset for Norwegian Dependency Parsing—0
DeepCCI: End-to-end Deep Learning for Chemical-Chemical Interaction PredictionCode0
Leveraging Patient Similarity and Time Series Data in Healthcare Predictive Models—0
Fast and Accurate Neural Word Segmentation for ChineseCode0
Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction—0
An entity-driven recursive neural network model for chinese discourse coherence modeling—0
Fast Learning and Prediction for Object Detection using Whitened CNN Features—0
Interpretation of Semantic Tweet RepresentationsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified