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 971–980 of 1706 papers

TitleStatusHype
Quaternion Factorization Machines: A Lightweight Solution to Intricate Feature Interaction Modelling—0
Query2Vec: An Evaluation of NLP Techniques for Generalized Workload Analytics—0
QUILL: Query Intent with Large Language Models using Retrieval Augmentation and Multi-stage Distillation—0
R2-D2: ColoR-inspired Convolutional NeuRal Network (CNN)-based AndroiD Malware Detections—0
RAGulator: Lightweight Out-of-Context Detectors for Grounded Text Generation—0
RAINER: A Robust Ensemble Learning Grid Search-Tuned Framework for Rainfall Patterns Prediction—0
Random CapsNet Forest Model for Imbalanced Malware Type Classification Task—0
Random Forests, Decision Trees, and Categorical Predictors: The "Absent Levels" Problem—0
randomHAR: Improving Ensemble Deep Learners for Human Activity Recognition with Sensor Selection and Reinforcement Learning—0
Rapid Classification of Crisis-Related Data on Social Networks using Convolutional Neural Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified