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 231–240 of 1706 papers

TitleStatusHype
PharmaGPT: Domain-Specific Large Language Models for Bio-Pharmaceutical and Chemistry—0
Comparing fingers and gestures for bci control using an optimized classical machine learning decoder—0
Horseshoe-type Priors for Independent Component Estimation—0
LightGBM robust optimization algorithm based on topological data analysis—0
PathoLM: Identifying pathogenicity from the DNA sequence through the Genome Foundation ModelCode0
Retrieval-Augmented Feature Generation for Domain-Specific Classification—0
Explainable AI for Comparative Analysis of Intrusion Detection ModelsCode0
Deep Learning Domain Adaptation to Understand Physico-Chemical Processes from Fluorescence Spectroscopy Small Datasets: Application to Ageing of Olive Oil—0
Optimized Feature Generation for Tabular Data via LLMs with Decision Tree ReasoningCode1
Enhancing Tabular Data Optimization with a Flexible Graph-based Reinforced Exploration Strategy—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified