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 171–180 of 1706 papers

TitleStatusHype
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
LML-DAP: Language Model Learning a Dataset for Data-Augmented PredictionCode1
Enhanced Convolution Neural Network with Optimized Pooling and Hyperparameter Tuning for Network Intrusion DetectionCode0
Reinforcement Feature Transformation for Polymer Property Performance Prediction—0
A Feature Engineering Approach for Literary and Colloquial Tamil Speech Classification using 1D-CNN—0
Investigation of Time-Frequency Feature Combinations with Histogram Layer Time Delay Neural Networks—0
Machine Learning for Public Good: Predicting Urban Crime Patterns to Enhance Community Safety—0
Leveraging Open-Source Large Language Models for Native Language Identification—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified