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 501–510 of 1706 papers

TitleStatusHype
Deep Interaction Machine: A Simple but Effective Model for High-order Feature Interactions—0
ADSAGE: Anomaly Detection in Sequences of Attributed Graph Edges applied to insider threat detection at fine-grained level—0
A Brief Survey of Machine Learning Methods for Emotion Prediction using Physiological Data—0
Decoding and interpreting cortical signals with a compact convolutional neural network—0
A Survey on Data Collection for Machine Learning: a Big Data -- AI Integration Perspective—0
Deep Learning based, end-to-end metaphor detection in Greek language with Recurrent and Convolutional Neural Networks—0
Decision Trees That Remember: Gradient-Based Learning of Recurrent Decision Trees with Memory—0
Decision Tree Based Wrappers for Hearing Loss—0
A Survey on Data-Centric AI: Tabular Learning from Reinforcement Learning and Generative AI Perspective—0
Amrita_CEN at SemEval-2022 Task 6: A Machine Learning Approach for Detecting Intended Sarcasm using Oversampling—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified