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 1601–1625 of 1706 papers

TitleStatusHype
A Survey on Recent Advances in Named Entity Recognition from Deep Learning modelsCode0
Fast and Accurate Neural Word Segmentation for ChineseCode0
Reproducible Machine Learning-based Voice Pathology Detection: Introducing the Pitch Difference FeatureCode0
Repurposing recidivism models for forecasting police officer use of forceCode0
Novelty Goes Deep. A Deep Neural Solution To Document Level Novelty DetectionCode0
Convolutional Neural Network with Word Embeddings for Chinese Word SegmentationCode0
Application of Machine Learning in Rock Facies Classification with Physics-Motivated Feature AugmentationCode0
Context-Based Tweet Engagement PredictionCode0
Numeric Encoding Options with AutomungeCode0
Responsive and Self-Expressive Dialogue GenerationCode0
Feature Interaction Aware Automated Data Representation TransformationCode0
YARE-GAN: Yet Another Resting State EEG-GANCode0
A Surprising Thing: The Application of Machine Learning Ensembles and Signal Theory to Predict Earnings SurprisesCode0
Ollivier persistent Ricci curvature (OPRC) based molecular representation for drug designCode0
Feature Engineering and Forecasting via Derivative-free Optimization and Ensemble of Sequence-to-sequence Networks with Applications in Renewable EnergyCode0
Automated Treatment Planning in Radiation Therapy using Generative Adversarial NetworksCode0
Solving the "false positives" problem in fraud predictionCode0
AutoM3L: An Automated Multimodal Machine Learning Framework with Large Language ModelsCode0
A Deep Learning Approach for Automatic Detection of Fake NewsCode0
Space-Time Representation of People Based on 3D Skeletal Data: A ReviewCode0
ReVal: A Simple and Effective Machine Translation Evaluation Metric Based on Recurrent Neural NetworksCode0
One Size Does Not Fit All: Multi-Scale, Cascaded RNNs for Radar ClassificationCode0
Advances in deep learning methods for pavement surface crack detection and identification with visible light visual imagesCode0
Text to Band Gap: Pre-trained Language Models as Encoders for Semiconductor Band Gap PredictionCode0
Revisiting neural relation classification in clinical notes with external informationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified