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Feature Engineering
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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.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/G-AILab/IGRM
공식 코드https://github.com/GAMES-UChile/mogptk
공식 코드https://github.com/GSLI25/GSLI25
공식 코드Graph Structure Learning for Spatial-Temporal Imputation: Adapting to Node and Feature Scales
https://github.com/GentleZhu/HEER
공식 코드Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks
https://github.com/Graph-Machine-Learning-Group/grin
공식 코드Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks
https://github.com/Graph-Machine-Learning-Group/peakweather-wind-forecasting
공식 코드PeakWeather: MeteoSwiss Weather Station Measurements for Spatiotemporal Deep Learning
https://github.com/Graph-Machine-Learning-Group/spin
공식 코드Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations
https://github.com/HDI-Project/ballet
공식 코드Enabling Collaborative Data Science Development with the Ballet Framework
https://github.com/HarikrishnanNB/genome_classification
공식 코드A Neurochaos Learning Architecture for Genome Classification
https://github.com/HarikrishnanNB/occd_experiments
공식 코드A Neurochaos Learning Architecture for Genome Classification
https://github.com/HoinJung/Unified-Debiaisng-VLM-SFID
공식 코드A Unified Debiasing Approach for Vision-Language Models across Modalities and Tasks
https://github.com/IoannisNasios/M5_Uncertainty_3rd_place
공식 코드https://github.com/IsmailAlaouiAbdellaoui/EQL-Wind-Speed-Forecasting
공식 코드Symbolic regression for scientific discovery: an application to wind speed forecasting
https://github.com/Ivanrs297/MACFE
공식 코드MACFE: A Meta-learning and Causality Based Feature Engineering Framework
https://github.com/Jacob-Zhou/stack-parser
공식 코드Is POS Tagging Necessary or Even Helpful for Neural Dependency Parsing?
https://github.com/JianqiaoMao/Interpretable-machine-learning-for-HVAC-predictive-control
공식 코드Interpreting Machine Learning Models for Room Temperature Prediction in Non-domestic Buildings
https://github.com/JiapengWu/TeMP
공식 코드TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion
https://github.com/Jiaxin-Xu/PairwiseShapley
공식 코드From Abstract to Actionable: Pairwise Shapley Values for Explainable AI
https://github.com/Jivnesh/SanDP
공식 코드https://github.com/JoonyoungYi/sparsity-normalization
공식 코드Why Not to Use Zero Imputation? Correcting Sparsity Bias in Training Neural Networks
https://github.com/Junqi-Zhang/AETN
공식 코드https://github.com/KIT-IAI/CopyPasteImputation
공식 코드https://github.com/KRR-Oxford/DeepOnto
공식 코드https://github.com/KoheiObata/MissNet
공식 코드Mining of Switching Sparse Networks for Missing Value Imputation in Multivariate Time Series
https://github.com/Lilyzhangyanlin/ELF-Gym
공식 코드ELF-Gym: Evaluating Large Language Models Generated Features for Tabular Prediction
https://github.com/LinghengMeng/LSTM-TD3
공식 코드https://github.com/LinglongQian/ExperimentalDesignAnalysis
공식 코드Unveiling the Secrets: How Masking Strategies Shape Time Series Imputation
https://github.com/Mai-CS/Deep-Convolutional-Forest
공식 코드Deep convolutional forest: a dynamic deep ensemble approach for spam detection in text
https://github.com/Mai-CS/enhanced-deep-convolutional-forest
공식 코드Deep convolutional forest: a dynamic deep ensemble approach for spam detection in text
https://github.com/MarkPKCollier/MissingDataVAEs
공식 코드https://github.com/Mengmi/Egocentric-Spatial-Memory
공식 코드https://github.com/MengyingLei/GLSKF
공식 코드https://github.com/Microsoft/EdgeML
공식 코드https://github.com/MohammadMahdiGhahramani7/MyPaper_DL_ML_Fin
공식 코드https://github.com/MysteryVaibhav/fake_news_semantics
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.