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 1551–1560 of 1706 papers

TitleStatusHype
SkipFlow: Incorporating Neural Coherence Features for End-to-End Automatic Text ScoringCode0
Recurrent neural networks with specialized word embeddings for health-domain named-entity recognitionCode0
Evaluating approaches for supervised semantic labelingCode0
Evaluating Large Language Models for Anxiety and Depression Classification using Counseling and Psychotherapy TranscriptsCode0
Evaluating the Effectiveness of Pre-trained Language Models in Predicting the Helpfulness of Online Product ReviewsCode0
Iterative Feature Boosting for Explainable Speech Emotion RecognitionCode0
Event Detection and Domain Adaptation with Convolutional Neural NetworksCode0
Recurrent Neural Networks with Stochastic Layers for Acoustic Novelty DetectionCode0
Catch: Collaborative Feature Set Search for Automated Feature EngineeringCode0
Activation Analysis of a Byte-Based Deep Neural Network for Malware ClassificationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified