Importerror: No module named sklearn.cross_validation
To quickly address the ImportError: replace the outdated cross_validation module with the more efficient model_selection module from scikit-learn.
Change this:
To this:
This simple adjustment is tailored for the newer versions of scikit-learn and fixes the error right away. Remember to keep your codebase current and follow the latest best practices.
Version compatibility and update
Updating scikit-learn
Keep your sklearn fresh as a daisy! Just run the following command:
You prefer Anaconda? No problem, use this:
Python: the older the version, the rustier it gets
Keep up with the times! Ensure you're using Python 3.x for improved package support and features.
To check your current Python version from your terminal:
Dust off your scikit-learn version
You can verify your sklearn version with:
If sklearn was a cheese and its version is below 0.20, it would probably be too ripe for consumption! Time to upgrade.
Installation integrity is no joke
Checking installation health
Corrupted installations are as much fun as a beach holiday in Siberia. Reinforce the integrity of your required libraries by reinstalling scikit-learn, numpy, and matplotlib.
Compatibility saga with your OS
Remember that your OS might not always play nice with others. Always confirm that the libraries are compatible with your operating system.
Shift to model_selection: embracing the new era
Embracing the new syntax
Cross over to the shiny new syntax and leave that ImportError in the dust of obsoletion. They say change is the only constant. That's especially true in coding!
Knowledge is power: Go through the docs
The scikit-learn documentation for model_selection is your gateway to untapping new abilities like advanced cross-validation strategies and hyper-parameter tuning.
The powerful global community
When in doubt, tap into the power of the sklearn community. Forums and chats are your go-to places for expert tips, tricks and how to avoid that problematic banana peel!
Was this article helpful?