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 401–410 of 1706 papers

TitleStatusHype
Neural Sentiment Classification with User and Product AttentionCode0
CLRGaze: Contrastive Learning of Representations for Eye Movement SignalsCode0
Neural Word Segmentation Learning for ChineseCode0
Novelty Goes Deep. A Deep Neural Solution To Document Level Novelty DetectionCode0
Danish Stance Classification and Rumour ResolutionCode0
DataStories at SemEval-2017 Task 4: Deep LSTM with Attention for Message-level and Topic-based Sentiment AnalysisCode0
CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical DataCode0
AutoFITS: Automatic Feature Engineering for Irregular Time SeriesCode0
On the Benefit of Combining Neural, Statistical and External Features for Fake News IdentificationCode0
An Embedding Learning Framework for Numerical Features in CTR PredictionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified