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 951–975 of 1706 papers

TitleStatusHype
Predicting Listing Prices In Dynamic Short Term Rental Markets Using Machine Learning Models—0
A Deep Learning Method for Predicting Mergers and Acquisitions: Temporal Dynamic Industry Networks—0
Predicting Polarities of Tweets by Composing Word Embeddings with Long Short-Term Memory—0
Predicting purchasing intent: Automatic Feature Learning using Recurrent Neural Networks—0
Predicting Structures in NLP: Constrained Conditional Models and Integer Linear Programming in NLP—0
Predicting the Industry of Users on Social Media—0
Predicting Vulnerability to Malware Using Machine Learning Models: A Study on Microsoft Windows Machines—0
Prediction Model For Wordle Game Results With High Robustness—0
Prediction of Stellar Age with the Help of Extra-Trees Regressor in Machine Learning—0
Prediction of the outcome of a Twenty-20 Cricket Match : A Machine Learning Approach—0
Predictive Precompute with Recurrent Neural Networks—0
PreGSU-A Generalized Traffic Scene Understanding Model for Autonomous Driving based on Pre-trained Graph Attention Network—0
Pre-trained Models or Feature Engineering: The Case of Dialectal Arabic—0
Print Defect Mapping with Semantic Segmentation—0
Product age based demand forecast model for fashion retail—0
Projective Quadratic Regression for Online Learning—0
Prompt Mechanisms in Medical Imaging: A Comprehensive Survey—0
Pseudo-Labels Are All You Need—0
QoSBERT: An Uncertainty-Aware Approach based on Pre-trained Language Models for Service Quality Prediction—0
Quantum Reinforcement Learning Trading Agent for Sector Rotation in the Taiwan Stock Market—0
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified