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 861–870 of 1706 papers

TitleStatusHype
Machine learning for complete intersection Calabi-Yau manifolds: a methodological studyCode0
Approaches to Fraud Detection on Credit Card Transactions Using Artificial Intelligence Methods—0
Low Dimensional State Representation Learning with Reward-shaped Priors—0
Iterative Boosting Deep Neural Networks for Predicting Click-Through Rate—0
Deep Learning based, end-to-end metaphor detection in Greek language with Recurrent and Convolutional Neural Networks—0
Supervised learning on heterogeneous, attributed entities interacting over time—0
Towards Ground Truth Explainability on Tabular DataCode1
When stakes are high: balancing accuracy and transparency with Model-Agnostic Interpretable Data-driven suRRogates—0
Evaluation Toolkit For Robustness Testing Of Automatic Essay Scoring SystemsCode1
ADSAGE: Anomaly Detection in Sequences of Attributed Graph Edges applied to insider threat detection at fine-grained level—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified