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 1401–1410 of 1706 papers

TitleStatusHype
A Deep Multi-View Learning Framework for City Event Extraction from Twitter Data Streams—0
Direct Multitype Cardiac Indices Estimation via Joint Representation and Regression LearningCode0
Spelling Correction as a Foreign Language—0
Shallow Updates for Deep Reinforcement Learning—0
Effective Representations of Clinical Notes—0
Learning a bidirectional mapping between human whole-body motion and natural language using deep recurrent neural networks—0
Practical Processing of Mobile Sensor Data for Continual Deep Learning Predictions—0
A Novel Neural Network Model for Joint POS Tagging and Graph-based Dependency ParsingCode0
R2-D2: ColoR-inspired Convolutional NeuRal Network (CNN)-based AndroiD Malware Detections—0
Drug-drug Interaction Extraction via Recurrent Neural Network with Multiple Attention Layers—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified