User guide#

skore helps at structuring and storing what matters in your data science experiments.

When it comes to data science, many libraries are available to help you investigate your data. pandas or polars are great tools to explore and transform your data. skrub is the one tool that brings the necessary statefulness to those transformations required by a machine learning pipeline (refer to skrub’s documentation). scikit-learn and other scikit-learn compatible libraries (e.g. xgboost, lightgbm) provide a set of algorithms to ingest transformed data and create predictive models, as well as tools to diagnose and evaluate them.

All these libraries are broad and generic by design in order to accommodate a wide range of use cases. It is your experience in choosing the appropriate building blocks from those libraries that is the key to success.

skore is the package that ties all these pieces together. It allows you to leverage your experience via a structured and robust framework for understanding the impact of your analysis choices, with seamless integration of all the above tools.

skore takes in the full data science pipeline created by assembling those building blocks and provides structured artifacts that store the information that matters for your particular use case. It minimizes unnecessary overhead spent on busywork, such as navigating through documentation, thereby streamlining the exploration process. skore reduces the amount of code required to show the information that matters, removing boilerplate code and making your project easier to understand and maintain. Finally, skore provides a way to store these structured artifacts in a project, allowing you to retrieve the results of your experiment whenever you need.

Table of contents#