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 971–980 of 1706 papers

TitleStatusHype
Expected F-Measure Training for Shift-Reduce Parsing with Recurrent Neural Networks—0
Explainable Adversarial Learning Framework on Physical Layer Secret Keys Combating Malicious Reconfigurable Intelligent Surface—0
Explainable AI Integrated Feature Engineering for Wildfire Prediction—0
Explainable Automatic Grading with Neural Additive Models—0
Explainable cognitive decline detection in free dialogues with a Machine Learning approach based on pre-trained Large Language Models—0
Explainable Multi-class Classification of Medical Data—0
Explainable Neural Networks based on Additive Index Models—0
Explaining Classifiers Trained on Raw Hierarchical Multiple-Instance Data—0
Explaining Translationese: why are Neural Classifiers Better and what do they Learn?—0
Exploiting Meta-Cognitive Features for a Machine-Learning-Based One-Shot Group-Decision Aggregation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified