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 1181–1190 of 1706 papers

TitleStatusHype
Rule-based vs. Neural Net Approaches to Semantic Textual Similarity—0
Enhanced Aspect Level Sentiment Classification with Auxiliary Memory—0
A Flexible and Easy-to-use Semantic Role Labeling Framework for Different Languages—0
A Review on Deep Learning Techniques Applied to Answer Selection—0
A Multi-Attention based Neural Network with External Knowledge for Story Ending Predicting Task—0
GBD-NER at PARSEME Shared Task 2018: Multi-Word Expression Detection Using Bidirectional Long-Short-Term Memory Networks and Graph-Based Decoding—0
Fast and Accurate Reordering with ITG Transition RNN—0
Seq2seq Dependency ParsingCode0
deepQuest: A Framework for Neural-based Quality EstimationCode0
Neural Math Word Problem Solver with Reinforcement Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified