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 1311–1320 of 1706 papers

TitleStatusHype
Multilingual and Multitarget Hate Speech Detection in Tweets—0
Multilingual discriminative lexicalized phrase structure parsing—0
Multimodal Deep Neural Networks using Both Engineered and Learned Representations for Biodegradability Prediction—0
Multi-Modal Video Feature Extraction for Popularity Prediction—0
Multi-output Headed Ensembles for Product Item Classification—0
Multi-Person Brain Activity Recognition via Comprehensive EEG Signal Analysis—0
Multi-Perspective Content Delivery Networks Security Framework Using Optimized Unsupervised Anomaly Detection—0
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified