Overview

This user guide will give you a detailled information on how to use flexcv functions and objects.

If you want to learn about nested cross validation in general and how we implemented it as a workflow in flexcv, check out our nested cross validation guide. This gives you a step by step overview of the process and explains the motivation behind nested cross validation.

You can learn about the neptune integration and tracking your experiments by checking out our neptune integration guide. You will learn how to use this super cool MLOps tool and leverage it's power to give you great insights in data and model performance.

Let's dive into how to fit a Random Forest Regressor on your data by tuning a hyperparameter in the inner cross validation and evaluate the model's performance in the outer cross validation.

Also, our guide on how to evaluate random effects takes you on the journey through the land of hierarchical data and gives an overview what to consider when facing machine learning problems that have a grouped structure. flexcv has some tools for that.

You might wonder, if your processes are influenced by randomness. Our guide on how to set up a repeated cross validation tackles this topic and shows how to use the RepeatedCV class with flexcv.

You still can not decide on the model type? No problem with flexcv since it allows to run multiple models in a single run. This guide shows how you set up the configuration for multiple models using different methods on the interface class or by storing it in a yaml file.