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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 1591–1600 of 1706 papers

TitleStatusHype
Comparing Word Representations for Implicit Discourse Relation Classification—0
Multi-Perspective Sentence Similarity Modeling with Convolutional Neural Networks—0
Sentence Modeling with Gated Recursive Neural Network—0
Fine-grained Opinion Mining with Recurrent Neural Networks and Word Embeddings—0
Chinese Semantic Role Labeling with Bidirectional Recurrent Neural Networks—0
Transition-based Dependency Parsing Using Two Heterogeneous Gated Recursive Neural Networks—0
Shallow Convolutional Neural Network for Implicit Discourse Relation Recognition—0
Machine Translation Evaluation using Recurrent Neural NetworksCode0
SHEF-NN: Translation Quality Estimation with Neural Networks—0
Hierarchical Low-Rank Tensors for Multilingual Transfer Parsing—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified