The advantage of this (over for example Gradient Boosting Quantile Regression) is that several quantiles can be predicted at once without the need for retraining the model, which overall leads to a significantly faster workflow. The quantile information is only used in the prediction phase. The models implemented here share the trait that they are trained in exactly the same way as their non-quantile counterpart. This means that practically the only dependency is sklearn and all its functionality is applicable to the here provided models without code changes.

I also tried to make an environment with mamba create -n myenv -c conda-forge scikit-learn, but this will install the standard CPython implementation as the interpreter.This module provides quantile machine learning models for python, in a plug-and-play fashion in the sklearn environment. I tried both with conda and pip install, but I got other errors about package conflicts. The environment can't be solved, aborting the operation Then i tried to install scikit-learn package with mamba install -n sklearn -c conda-forge scikit-learn and i get this output: Looking for:

Type 'help', 'copyright', 'credits' or 'license' for more information. In fact, running python inside the shell after env activation returns: Python 3.9.16 | packaged by conda-forge | (feeb267e, Jan 18 2023, 16:13:01) Mamba create -n sklearn -c conda-forge pypy python I succeded in the environment creation and setup with:

I'm trying to create a conda env (actually I'm using Mamba) whith PyPy as default interpreter and then installing Scikit-Learn to perform machine-learning tasks.

Conda install sklearn