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 1026–1050 of 1706 papers

TitleStatusHype
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
Scheduled Knowledge Acquisition on Lightweight Vector Symbolic Architectures for Brain-Computer Interfaces—0
Schooling to Exploit Foolish Contracts—0
Scientific Computing with Diffractive Optical Neural Networks—0
SciREL at SemEval-2018 Task 7: A System for Semantic Relation Extraction and Classification—0
Scoring Root Necrosis in Cassava Using Semantic Segmentation—0
See it, Think it, Sorted: Large Multimodal Models are Few-shot Time Series Anomaly Analyzers—0
Segmentation of Argumentative Texts with Contextualised Word Representations—0
Selectivity Estimation for Range Predicates using Lightweight Models—0
Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlooks—0
Self-FuseNet: Data Free Unsupervised Remote Sensing Image Super-Resolution—0
Self-Optimizing Feature Transformation—0
Self-Reasoning Assistant Learning for non-Abelian Gauge Fields Design—0
Self-Supervised Learning for Modeling Gamma-ray Variability in Blazars—0
Self-training improves Recurrent Neural Networks performance for Temporal Relation Extraction—0
Semantic Annotation for Tabular Data—0
Semantic Frame Labeling with Target-based Neural Model—0
Semantic-Guided RL for Interpretable Feature Engineering—0
Semantic Linking in Convolutional Neural Networks for Answer Sentence Selection—0
Semantic Loss Application to Entity Relation Recognition—0
SEM-CLIP: Precise Few-Shot Learning for Nanoscale Defect Detection in Scanning Electron Microscope Image—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified