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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 1521–1530 of 1706 papers

TitleStatusHype
Chemical-Induced Disease Detection Using Invariance-based Pattern Learning Model—0
Chinese Event Extraction Using DeepNeural Network with Word Embedding—0
Chinese Grammatical Error Diagnosis Based on CRF and LSTM-CRF model—0
Chinese Semantic Role Labeling with Bidirectional Recurrent Neural Networks—0
Chinese Zero Pronoun Resolution with Deep Memory Network—0
Chinese Zero Pronoun Resolution with Deep Neural Networks—0
Chronic Diseases Prediction Using ML—0
CIDMP: Completely Interpretable Detection of Malaria Parasite in Red Blood Cells using Lower-dimensional Feature Space—0
Citcom – Citation Recommendation—0
Classification of Electrical Impedance Tomography Data Using Machine Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified