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 291–300 of 1706 papers

TitleStatusHype
Statistical Test for Auto Feature Engineering by Selective InferenceCode0
Sui Generis: Large Language Models for Authorship Attribution and Verification in Latin—0
Towards Trustworthy Web Attack Detection: An Uncertainty-Aware Ensemble Deep Kernel Learning Model—0
Principal Orthogonal Latent Components Analysis (POLCA Net)Code0
Neural-Bayesian Program Learning for Few-shot Dialogue Intent Parsing—0
Learning to Solve Abstract Reasoning Problems with Neurosymbolic Program Synthesis and Task Generation—0
Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlooks—0
Semantic-Guided RL for Interpretable Feature Engineering—0
Enhancing End Stage Renal Disease Outcome Prediction: A Multi-Sourced Data-Driven Approach—0
Automatic deductive coding in discourse analysis: an application of large language models in learning analyticsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified