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 1451–1460 of 1706 papers

TitleStatusHype
Exploration of Proximity Heuristics in Length Normalization—0
Deep-HiTS: Rotation Invariant Convolutional Neural Network for Transient DetectionCode0
Training End-to-End Dialogue Systems with the Ubuntu Dialogue Corpus—0
Learning Feature Engineering for Classification—0
Graph Convolutional Networks for Named Entity RecognitionCode0
Sparse Coding of Neural Word Embeddings for Multilingual Sequence Labeling—0
Predicting the Industry of Users on Social Media—0
Towards Wide Learning: Experiments in HealthcareCode0
Task-Guided and Path-Augmented Heterogeneous Network Embedding for Author IdentificationCode0
We used Neural Networks to Detect Clickbaits: You won't believe what happened Next!Code0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified