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 951–960 of 1706 papers

TitleStatusHype
Enhancing Generalizability of Predictive Models with Synergy of Data and Physics—0
Enhancing IoT Security: A Novel Feature Engineering Approach for ML-Based Intrusion Detection Systems—0
Enhancing Molecular Design through Graph-based Topological Reinforcement Learning—0
Enhancing Physics-Informed Neural Networks Through Feature Engineering—0
Enhancing Tabular Data Optimization with a Flexible Graph-based Reinforced Exploration Strategy—0
Enhancing Wind Speed and Wind Power Forecasting Using Shape-Wise Feature Engineering: A Novel Approach for Improved Accuracy and Robustness—0
Enriching Tabular Data with Contextual LLM Embeddings: A Comprehensive Ablation Study for Ensemble Classifiers—0
Ensemble learning of diffractive optical networks—0
Ensemble Learning to Assess Dynamics of Affective Experience Ratings and Physiological Change—0
ERNIE at SemEval-2020 Task 10: Learning Word Emphasis Selection by Pre-trained Language Model—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified