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 1121–1130 of 1706 papers

TitleStatusHype
Team_BUDDI at ComMA@ICON: Exploring Individual and Joint Modelling Approaches for Detecting Aggression, Communal Bias and Gender Bias—0
Techniques for Automated Machine Learning—0
TEET! Tunisian Dataset for Toxic Speech Detection—0
Temperature Distribution Prediction in Laser Powder Bed Fusion using Transferable and Scalable Graph Neural Networks—0
Temporal-Aware Graph Attention Network for Cryptocurrency Transaction Fraud Detection—0
Temporal Spatial Decomposition and Fusion Network for Time Series Forecasting—0
Temporal Tensor Transformation Network for Multivariate Time Series Prediction—0
Test Automation with Grad-CAM Heatmaps -- A Future Pipe Segment in MLOps for Vision AI?—0
Text2shape Deep Retrieval Model: Generating Initial Cases for Mechanical Part Redesign under the Context of Case-Based Reasoning—0
Text embedding models can be great data engineers—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified