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 1211–1220 of 1706 papers

TitleStatusHype
Rumor Detection on Twitter with Tree-structured Recursive Neural NetworksCode0
Syntax for Semantic Role Labeling, To Be, Or Not To BeCode0
Stock Movement Prediction from Tweets and Historical PricesCode0
Product-based Neural Networks for User Response Prediction over Multi-field Categorical DataCode0
Semi-supervised Seizure Prediction with Generative Adversarial Networks—0
A Simple Fusion of Deep and Shallow Learning for Acoustic Scene ClassificationCode0
Binary Classification in Unstructured Space With Hypergraph Case-Based ReasoningCode0
ServeNet: A Deep Neural Network for Web Services ClassificationCode0
Extracting Parallel Sentences with Bidirectional Recurrent Neural Networks to Improve Machine TranslationCode0
Explainable Neural Networks based on Additive Index Models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified