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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/andrew-cr/jump-diffusion
공식 코드Trans-Dimensional Generative Modeling via Jump Diffusion Models
https://github.com/andrisger/feature-engineering-with-regularity-structures
공식 코드https://github.com/anewgithubname/gradest2
공식 코드https://github.com/anjieliu121/well_performance_optimization
공식 코드Machine Learning-Based Completions Sequencing for Well Performance Optimization
https://github.com/annawangkkk/Landmine-risk-prediction
공식 코드RELand: Risk Estimation of Landmines via Interpretable Invariant Risk Minimization
https://github.com/anonsc5kdd/sc5
공식 코드Joint Analysis of Single-Cell Data across Cohorts with Missing Modalities
https://github.com/anonymous-author-2021/omasgan
공식 코드OMASGAN: Out-of-Distribution Minimum Anomaly Score GAN for Sample Generation on the Boundary
https://github.com/anonymous-research-entity/recsys2022-cqfs-ttopt
공식 코드https://github.com/antonio955/pi_dae
공식 코드https://github.com/aperezlebel/benchmark_mv_approaches
공식 코드Benchmarking missing-values approaches for predictive models on health databases
https://github.com/aquemy/HCBR
공식 코드Binary Classification in Unstructured Space With Hypergraph Case-Based Reasoning
https://github.com/arezaz/ensemble-binary-classification
공식 코드A Generalized Flow for B2B Sales Predictive Modeling: An Azure Machine Learning Approach
https://github.com/atong01/conditional-flow-matching
공식 코드Generating and Imputing Tabular Data via Diffusion and Flow-based Gradient-Boosted Trees
https://github.com/atschalz/dc_tabeval
공식 코드A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular Data
https://github.com/aubreybirdwell/2024-fie-unsuccessful-students
공식 코드Detecting Unsuccessful Students in Cybersecurity Exercises in Two Different Learning Environments
https://github.com/audesportisse/clustering-mnar
공식 코드https://github.com/automl/caafe
공식 코드https://github.com/automl/llms_feature_engineering_bias
공식 코드Large Language Models Engineer Too Many Simple Features For Tabular Data
https://github.com/awrgold/g-rank
공식 코드G-Rank: Unsupervised Continuous Learn-to-Rank for Edge Devices in a P2P Network
https://github.com/b010001y/prodiff
공식 코드ProDiff: Prototype-Guided Diffusion for Minimal Information Trajectory Imputation
https://github.com/bahramjafrasteh/missinggps
공식 코드https://github.com/bai-lab/kd-debias
공식 코드Invariant debiasing learning for recommendation via biased imputation
https://github.com/baichuan-inc/baichuan2
공식 코드https://github.com/baiyang4/Sjogrens_questionnaire
공식 코드https://github.com/ballet/ballet
공식 코드Enabling Collaborative Data Science Development with the Ballet Framework
https://github.com/bargavj/EvaluatingDPML
공식 코드https://github.com/basiralab/fedgmte-net-plus
공식 코드Predicting Infant Brain Connectivity with Federated Multi-Trajectory GNNs using Scarce Data
https://github.com/bcjaeger/Imputation-and-CV
공식 코드When to Impute? Imputation before and during cross-validation
https://github.com/bdwilliamson/flevr
공식 코드https://github.com/bdwilliamson/flevr_supplementary
공식 코드https://github.com/bestadcarry/mi-nngp
공식 코드Multiple Imputation with Neural Network Gaussian Process for High-dimensional Incomplete Data
https://github.com/bghojogh/Feature-Extraction-Survey
공식 코드Feature Selection and Feature Extraction in Pattern Analysis: A Literature Review
https://github.com/bicycleman15/skim
공식 코드On the Necessity of World Knowledge for Mitigating Missing Labels in Extreme Classification
https://github.com/bigdata-ustc/XCrossNet
공식 코드XCrossNet: Feature Structure-Oriented Learning for Click-Through Rate Prediction
https://github.com/bioannidis/Coupled_tensors_graphs
공식 코드Coupled Graphs and Tensor Factorization for Recommender Systems and Community Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.