“feature-engineering”的搜索结果

Feature construction from market data for ML trading models including price, volume, on-chain, and microstructure features

中文简介正在准备中。当前显示原文说明。

Use when building or changing features, preprocessing, encoders, resampling or train/validation/test splits — keep every fitted transform inside a Pipeline fitted on training folds only, and build time and group features without seeing the future or the same entity twice

中文简介正在准备中。当前显示原文说明。

Design or audit a feature store — serving, freshness, and sharing across models. Use when asked "do we need a feature store", "design a feature store", or "share features across models".

中文简介正在准备中。当前显示原文说明。

Audit feature engineering code for leakage, quality issues, and pipeline correctness. Use when asked to "audit our feature pipeline", "is there data leakage", or "find feature quality issues".

中文简介正在准备中。当前显示原文说明。

Design and implement a feature engineering pipeline for a ML problem. Use when asked to "engineer features for this model", "what features should we build", or "design feature transformations".

中文简介正在准备中。当前显示原文说明。

Build a feature store using Feast for centralized feature management, configure offline and online stores for batch and real-time serving, define feature views with transformations, and implement point-in-time correct joins for ML pipelines. Use when managing features for multiple ML models, ensuring training-serving consistency, serving low-latency features for real-time inference, reusing feature definitions across projects, or building a feature catalog for discovery and governance.

中文简介正在准备中。当前显示原文说明。

Design and implement features from raw data for machine learning models. Use when the user says "engineer features", "feature engineering for X", "create features from this dataset", "transform these variables", "encode categoricals", "handle skew", "create interaction features", "lag features", "extract features from text or dates", "improve model performance with better features", or wants to move from raw columns to model-ready inputs.

中文简介正在准备中。当前显示原文说明。

Expert en data science (pandas, numpy, scikit-learn, feature engineering, model selection)

中文简介正在准备中。当前显示原文说明。

Data Scientist IA — Expert en data science (pandas, numpy, scikit-learn, EDA, feature engineering, statistical analysis)

中文简介正在准备中。当前显示原文说明。