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 81–90 of 1706 papers

TitleStatusHype
Chronic Diseases Prediction Using ML—0
LLM4GNAS: A Large Language Model Based Toolkit for Graph Neural Architecture Search—0
A Survey on Data-Centric AI: Tabular Learning from Reinforcement Learning and Generative AI Perspective—0
Decision Tree Based Wrappers for Hearing Loss—0
Exploring Patterns Behind Sports—0
Enhancing Physics-Informed Neural Networks Through Feature Engineering—0
Application of quantum machine learning using quantum kernel algorithms on multiclass neuron M type classification—0
Agentic AI Systems Applied to tasks in Financial Services: Modeling and model risk management crews—0
Decision Trees That Remember: Gradient-Based Learning of Recurrent Decision Trees with Memory—0
From Features to Transformers: Redefining Ranking for Scalable Impact—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified