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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 371–380 of 1706 papers

TitleStatusHype
Application of quantum machine learning using quantum kernel algorithms on multiclass neuron M type classification—0
Chemellia: An Ecosystem for Atomistic Scientific Machine Learning—0
Chemical-Induced Disease Detection Using Invariance-based Pattern Learning Model—0
Horseshoe-type Priors for Independent Component Estimation—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
An Interactive Web-Interface for Visualizing the Inner Workings of the Question Answering LSTM—0
Bacteria and Biotope Entity Recognition Using A Dictionary-Enhanced Neural Network Model—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified