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 851–860 of 1706 papers

TitleStatusHype
Multi-Perspective Sentence Similarity Modeling with Convolutional Neural Networks—0
Multi-Scale Control Signal-Aware Transformer for Motion Synthesis without Phase—0
Multi-Scale DenseNet-Based Electricity Theft Detection—0
Multi-Statistic Approximate Bayesian Computation with Multi-Armed Bandits—0
Multi-task and Multi-lingual Joint Learning of Neural Lexical Utterance Classification based on Partially-shared Modeling—0
Multi-Task Bidirectional Transformer Representations for Irony Detection—0
Multi-Task Cross-Lingual Sequence Tagging from Scratch—0
Multitask Learning with Deep Neural Networks for Community Question Answering—0
Multi-View Feature Engineering and Learning—0
Multi-View Semantic Labeling of 3D Point Clouds for Automated Plant Phenotyping—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified