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 561–570 of 1706 papers

TitleStatusHype
Differentiable Sparsification for Deep Neural Networks—0
Detection of Unknown Anomalies in Streaming Videos with Generative Energy-based Boltzmann Models—0
Detection of Product Comparisons - How Far Does an Out-of-the-Box Semantic Role Labeling System Take You?—0
Discourse Parsing with Attention-based Hierarchical Neural Networks—0
Automated Pavement Crack Segmentation Using U-Net-based Convolutional Neural Network—0
Automatic Analysis of Linguistic Features in Journal Articles of Different Academic Impacts with Feature Engineering Techniques—0
An End-to-End Graph Convolutional Kernel Support Vector Machine—0
Distilled Mid-Fusion Transformer Networks for Multi-Modal Human Activity Recognition—0
Distinguishing Risk Preferences using Repeated Gambles—0
An Empirical Study of Factors Affecting Language-Independent Models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified