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 1271–1280 of 1706 papers

TitleStatusHype
Machine Learning-Based Prediction of Mortality in Geriatric Traumatic Brain Injury Patients—0
Machine Learning-Based Prediction of Key Genes Correlated to the Subretinal Lesion Severity in a Mouse Model of Age-Related Macular Degeneration—0
Machine Learning - Driven Materials Discovery: Unlocking Next-Generation Functional Materials -- A minireview—0
Machine Learning for Detecting Data Exfiltration: A Review—0
Machine Learning for Public Good: Predicting Urban Crime Patterns to Enhance Community Safety—0
Machine Learning for the Detection and Identification of Internet of Things (IoT) Devices: A Survey—0
Machine Learning Framework for Audio-Based Content Evaluation using MFCC, Chroma, Spectral Contrast, and Temporal Feature Engineering—0
Machine Learning in LiDAR 3D point clouds—0
Maintaining and Managing Road Quality:Using MLP and DNN—0
Making forecasting self-learning and adaptive -- Pilot forecasting rack—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified