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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 1361–1370 of 1706 papers

TitleStatusHype
An entity-driven recursive neural network model for chinese discourse coherence modeling—0
An Error Analysis Tool for Natural Language Processing and Applied Machine Learning—0
An Error Correction Mid-term Electricity Load Forecasting Model Based on Seasonal Decomposition—0
A Neural Autoencoder Approach for Document Ranking and Query Refinement in Pharmacogenomic Information Retrieval—0
A Neural Network Based Explainable Recommender System—0
A neural network model for solvency calculations in life insurance—0
A New Psychometric-inspired Evaluation Metric for Chinese Word Segmentation—0
An Examination of Wearable Sensors and Video Data Capture for Human Exercise Classification—0
An Exponential Factorization Machine with Percentage Error Minimization to Retail Sales Forecasting—0
An Interactive Web-Interface for Visualizing the Inner Workings of the Question Answering LSTM—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified