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 191–200 of 1706 papers

TitleStatusHype
A Progressive Transformer for Unifying Binary Code Embedding and Knowledge Transfer—0
A Process for the Evaluation of Node Embedding Methods in the Context of Node Classification—0
A Deep Learning Approach for Macroscopic Energy Consumption Prediction with Microscopic Quality for Electric Vehicles—0
Approximation Ratios of Graph Neural Networks for Combinatorial Problems—0
Arabic Diacritic Recovery Using a Feature-Rich biLSTM Model—0
Arabic Named Entity Recognition: What Works and What's Next—0
Arabic POS Tagging: Don't Abandon Feature Engineering Just Yet—0
A Joint Model for Chinese Microblog Sentiment Analysis—0
A Kernel Two-sample Test for Dynamical Systems—0
Approaches to Fraud Detection on Credit Card Transactions Using Artificial Intelligence Methods—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified