❗ if you encounter any error with the instructions given on this page, please create a github issue to explain your problem and we will try to get back to you ASAP.
We detail in this page how to set up your environment with the different external modules you will need in order to be able to follow the course.
We recommend you create a new conda environment specifically for the course (if you are unfamiliar with conda environment see this documentation).
Nevertheless, we detail here several methods a trust you will choose the one most appropriate to your situations.
important: the course materials were developped and tested with python >=3.11 and scikit-learn >=1.5. Any anterior version will give errors and warnings aplenty!
Download the file intermediateML_2024.yml.
If you are on Windows and/or are allergic to command line, you can use the anaconda navigator (if you don't know how to start the navigator, here's how).
Otherwise, just open a terminal, navigate to where the file is, and use the following command:
conda env create -f intermediateML_2024.yml
Activate the new environment: conda activate intermediateML_2024
Verify that the new environment was installed correctly: conda env list
These first 2 commands create and activate a new enviroment
conda create -y -n intermediateML_2024 python=3.11
conda activate intermediateML_2024
These commands install all necessary modules and their dependencies:
pip install shap xgboost-cpu seaborn ipykernel hyperopt scikit-learn
Python : at least 3.11
- shap
- xgboost-cpu
- seaborn
- ipykernel
- hyperopt
- scikit-learn