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 1611–1620 of 1706 papers

TitleStatusHype
Deep Interaction Machine: A Simple but Effective Model for High-order Feature Interactions—0
Deep Learning-Based Detection of the Acute Respiratory Distress Syndrome: What Are the Models Learning?—0
Deep Learning based, end-to-end metaphor detection in Greek language with Recurrent and Convolutional Neural Networks—0
Deep Learning-Based Forecasting of Boarding Patient Counts to Address ED Overcrowding—0
Deep Learning for Chinese Word Segmentation and POS Tagging—0
Deep Learning for Effective and Efficient Reduction of Large Adaptation Spaces in Self-Adaptive Systems—0
Deep Learning for Insider Threat Detection: Review, Challenges and Opportunities—0
Deep Learning for Iris Recognition: A Review—0
Deep Learning for NLP (without Magic)—0
Deep Learning Head Model for Real-time Estimation of Entire Brain Deformation in Concussion—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified