What are the pre-requisites for scikit-learn?

To install and use scikit-learn, there are several pre-requisites and dependencies you need to ensure are in place:

Python Version

• Scikit-learn requires Python 3.6 or later. You can check your Python version using the command:

  python3 --version

Dependencies

• NumPy: Scikit-learn requires NumPy version 1.16.0 or later124.
• SciPy: It also requires SciPy version 1.0.0 or later124.

Installation Environment

• It is highly recommended to use a virtual environment to avoid conflicts with other packages. You can create a virtual environment using venv or conda:

  python -m venv sklearn-env
  source sklearn-env/bin/activate
  pip install -U scikit-learn

  or

  conda create -n sklearn-env -c conda-forge scikit-learn
  conda activate sklearn-env

Additional Packages for Certain Features

• For plotting capabilities, Matplotlib is required. Some examples may also require scikit-image, pandas, or seaborn1.

Compiler and Development Headers (For Source Installation)

• If you choose to install scikit-learn from source, you will need a working C/C++ compiler and Python development headers. For example, on Debian-based systems, you can install these using:

  sudo apt-get install build-essential python3-dev python3-setuptools python3-numpy python3-scipy libatlas-dev libatlas3gf-base

By ensuring these pre-requisites are met, you can successfully install and use scikit-learn for your machine learning tasks.