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 681–690 of 1706 papers

TitleStatusHype
A Comparison of SVM against Pre-trained Language Models (PLMs) for Text Classification Tasks—0
Character-Aware Neural Networks for Arabic Named Entity Recognition for Social Media—0
Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide—0
Application of federated learning techniques for arrhythmia classification using 12-lead ECG signals—0
A Hybrid Approach for Smart Alert Generation—0
Dynamic Bayesian Networks for Predicting Cryptocurrency Price Directions: Uncovering Causal Relationships—0
Application of Clinical Concept Embeddings for Heart Failure Prediction in UK EHR data—0
Application of Artificial Intelligence in Schizophrenia Rehabilitation Management: A Systematic Scoping Review—0
Aggression Detection in Social Media: Using Deep Neural Networks, Data Augmentation, and Pseudo Labeling—0
A Deep Belief Network Based Machine Learning System for Risky Host Detection—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified