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 811–820 of 1706 papers

TitleStatusHype
Symmetry-adapted graph neural networks for constructing molecular dynamics force fields—0
Improving DGA-Based Malicious Domain Classifiers for Malware Defense with Adversarial Machine Learning—0
Detecting Singleton Spams in Reviews via Learning Deep Anomalous Temporal Aspect-Sentiment PatternsCode0
String Theory: Parsed Categoric Encodings with Automunge—0
Reusing Preprocessing Data as Auxiliary Supervision in Conversational Analysis—0
A Numbers Game: Numeric Encoding Options with Automunge—0
Simple deductive reasoning tests and data sets for exposing limitation of today's deep neural networks—0
Enhancing Sindhi Word Segmentation using Subword Representation Learning and Position-aware Self-attention—0
Advances in deep learning methods for pavement surface crack detection and identification with visible light visual imagesCode0
Shape-based Feature Engineering for Solar Flare Prediction—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified