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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 1001–1010 of 1706 papers

TitleStatusHype
Reusable workflows for gender prediction—0
Reuse and Adaptation for Entity Resolution through Transfer Learning—0
Reusing Preprocessing Data as Auxiliary Supervision in Conversational Analysis—0
Review of automated time series forecasting pipelines—0
Revisiting Character-Based Neural Machine Translation with Capacity and Compression—0
Revisiting the Role of Feature Engineering for Compound Type Identification in Sanskrit—0
RF-LighGBM: A probabilistic ensemble way to predict customer repurchase behaviour in community e-commerce—0
Robust cross-domain disfluency detection with pattern match networks—0
Robust Domain Adaptation for Relation Extraction via Clustering Consistency—0
Robust Event Classification Using Imperfect Real-world PMU Data—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified