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 1391–1400 of 1706 papers

TitleStatusHype
Deep Learning-Based Noninvasive Screening of Type 2 Diabetes with Chest X-ray Images and Electronic Health RecordsCode0
Machine Learning Pipelines with Modern Big Data Tools for High Energy PhysicsCode0
Machine Translation Evaluation using Recurrent Neural NetworksCode0
Self-regulation: Employing a Generative Adversarial Network to Improve Event DetectionCode0
An Embedding Learning Framework for Numerical Features in CTR PredictionCode0
deepQuest: A Framework for Neural-based Quality EstimationCode0
Malicious Network Traffic Detection via Deep Learning: An Information Theoretic ViewCode0
BigGreen at SemEval-2021 Task 1: Lexical Complexity Prediction with Assembly ModelsCode0
Hierarchical Attention Based Position-Aware Network for Aspect-Level Sentiment AnalysisCode0
Malware Classification using Deep Learning based Feature Extraction and Wrapper based Feature Selection TechniqueCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified