SOTAVerified

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 1021–1030 of 1706 papers

TitleStatusHype
RUL forecasting for wind turbine predictive maintenance based on deep learning—0
RULLS: Randomized Union of Locally Linear Subspaces for Feature Engineering—0
SAFE: Scalable Automatic Feature Engineering Framework for Industrial Tasks—0
Sales Forecast in E-commerce using Convolutional Neural Network—0
Sample-Efficient Behavior Cloning Using General Domain Knowledge—0
Sanskrit Word Segmentation Using Character-level Recurrent and Convolutional Neural Networks—0
SA-UZH: Verb-based Sentiment Analysis—0
Scalable and Interpretable Contextual Bandits: A Literature Review and Retail Offer Prototype—0
Scalable Deployment of AI Time-series Models for IoT—0
Scalable, Distributed AI Frameworks: Leveraging Cloud Computing for Enhanced Deep Learning Performance and Efficiency—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified