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 1051–1060 of 1706 papers

TitleStatusHype
Semi-Supervised Convolutional Neural Networks for Human Activity Recognition—0
Semi-supervised Seizure Prediction with Generative Adversarial Networks—0
Semi-Supervised Semantic Role Labeling with Cross-View Training—0
Sensitive Data Detection and Classification in Spanish Clinical Text: Experiments with BERT—0
Sentence Level Human Translation Quality Estimation with Attention-based Neural Networks—0
Sentence Modeling with Gated Recursive Neural Network—0
Sentiment analysis and random forest to classify LLM versus human source applied to Scientific Texts—0
Sentiment Analysis of Arabic Tweets: Feature Engineering and A Hybrid Approach—0
Sentiment Analysis of Political Tweets: Towards an Accurate Classifier—0
SEPT: Towards Efficient Scene Representation Learning for Motion Prediction—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified