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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 661–670 of 1706 papers

TitleStatusHype
Intelligent Approaches to Predictive Analytics in Occupational Health and Safety in India—0
Chinese Zero Pronoun Resolution with Deep Memory Network—0
Chinese Semantic Role Labeling with Bidirectional Recurrent Neural Networks—0
Applications of Large Language Model Reasoning in Feature Generation—0
Chinese Grammatical Error Diagnosis Based on CRF and LSTM-CRF model—0
Chinese Event Extraction Using DeepNeural Network with Word Embedding—0
Application Research On Real-Time Perception Of Device Performance Status—0
A Hybrid Quantum Classical Pipeline for X Ray Based Fracture Diagnosis—0
Chemical-Induced Disease Detection Using Invariance-based Pattern Learning Model—0
Application of Statistical Relational Learning to Hybrid Recommendation Systems—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified