You might face ImportError: No module namedsklearn.cross_validation error when working with sklearn module. The error occurs even if you have installed sklearn on your system. Usually, we face this problem, when we tried to import train and test split from sklearn.cross_validation. In this short article, we will learn how we can solve ImportError: No module namedsklearn.cross_validation using various methods. We will also discuss how to understand such errors so that in the future we will be able to solve them easily.
ImportError: No module namedsklearn.cross_validation – Possible solutions
The error ImportError: No module namedsklearn.cross_validation occurs when you tried to import the train and test split function from the sklearn module. Although you have successfully installed sklearn module but still getting this error:
# importing train test split function from sklearn.cross_validation import train_test_split
ImportError: No module named sklearn.cross_validation
The reason for getting this error is because the sklearn.cross_validation is no more active in the sklearn module. Instead, now, there is model_selection which can be used to import train_test_split.
In this short article, will go through the following possible solutions:
- Using model_selection
- Degrading sklearn module
- Alternative methods
- Installing sklearn module
Now let us jump into the possible solutions and explain each one with examples.
Solution-1: Using model_selection
As we said coss_validation module is not more active in sklearn module. In order to get the train_test_split function, we have to import it from sklearn.model_selection as shown below:
#importing the submodule from sklearn.model_selection import train_test_split
When you run the above code and remove the previous import, you will get rid of the problem.
Solution-2: Degrading the sklearn module
As we discussed that the cross-validation module is no more active in the new versions of sklearn but it is still working in the old versions. So, if you for some cases need to run the code with an older version, but get ImportError: No module namedsklearn.cross_validation error then you can degrade your module.
You can uninstall the module and then install the old version on your system.
# uninstalling the module pip uninstall scikit-learn # using pip3 pip3 uninstall scikit-learn # installing specific version pip install scikit-learn==0.18
This method is not recommended but can be used as an alternative.
Solution-3: Alternative method
If you have code that needs to run various versions you could do something like this in order to get rid of the error:
# importing the sklearn module import sklearn # using if-else to check the versions if sklearn.__version__ > '0.18': from sklearn.model_selection import train_test_split else: from sklearn.cross_validation import train_test_split
Or here is a better version of the same method:
from packaging.version import parse import sklearn if parse(sklearn.__version__) > parse('0.18'): from sklearn.model_selection import train_test_split else: from sklearn.cross_validation import train_test_split
This method is also not recommended but can be used as an alternative.
Solution-4: Installing sklearn module
If you haven’t installed the sklearn module on your system yet then you can use any of the following methods to install the module and then import the train_test_split function.
Installing using pip command:
# using Python 2 pip install scikit-learn scipy matplotlib numpy # pip3.10 pip3 install scikit-learn scipy matplotlib numpy
If the pip command is not in the path then you can use the following commands:
# if you don't have pip in your PATH environment variable python -m pip install scikit-learn scipy matplotlib numpy # for python 3 python3 -m pip install scikit-learn scipy matplotlib numpy
Alternatively, you can use the following commands as well:
py -m pip install scikit-learn scipy matplotlib numpy # if you get permissions error sudo pip3 install scikit-learn scipy matplotlib numpy pip install scikit-learn scipy matplotlib numpy --user # for Anaconda conda install -c anaconda scikit-learn # for Jupyter Notebook !pip install scikit-learn scipy matplotlib numpy
Hopefully, these methods will help you to get rid of ImportError: No module namedsklearn.cross_validation error.
Understanding the ImportError: No module namedsklearn.cross_validation error error
In Python, the errors give a lot of information about themselves. Usually, there are two main parts of errors in Python. The first part gives information about the type of error which in this case is ImportError which means there the script couldn’t import some modules. While the second part of the error gives more specific information about the error which in this case helps us to figure out which module is not being imported by the script.
Reasons for getting ImportError: No module namedsklearn.cross_validation error error
The ImportError “No module named ‘sklearn.cross_validation'” is raised when Python cannot find the module “sklearn.cross_validation” in its search path. Here are some possible reasons for this error:
- Sklearn version mismatch:
cross_validationwas moved to
model_selectionmodule in sklearn 0.18. If you are using a version of scikit-learn that is older than 0.18, you should use
sklearn.cross_validation, otherwise you should use
- Incorrect import statement: Ensure that you have imported the module correctly. It should be
from sklearn.model_selection import cross_validatefor versions >=0.18 and
from sklearn.cross_validation import cross_val_scorefor versions <0.18
- Missing installation: If the module is not found, it may not be installed in your system. You can install it using pip:
pip install scikit-learn
- Virtual environment: If you are using a virtual environment, make sure that you have installed scikit-learn within the virtual environment, and that you have activated the virtual environment before running your Python script.
- Path issues: Ensure that the location of the module is in your system’s path. You can check this by running
import sys; print(sys.path)in your Python console or script.
- Name collision: It’s possible that you have created a file named “sklearn.py” in the same directory as your script. If so, Python may be importing this file instead of the actual “sklearn” module, leading to the “No module named ‘sklearn.cross_validation'” error.
- System configuration: In rare cases, this error may be caused by issues with your system configuration, such as missing or outdated dependencies. You may need to update your system or reinstall Python to fix the issue.
What is ImportError in Python?
This error typically appears when one of the following prevents the import of a class: There is a cyclic dependency on the imported class. The imported class either doesn’t exist or wasn’t made. The class name imported is spelled incorrectly. The module’s imported class is in the wrong place.
What is the Sklearn module?
In the Python ecosystem, Scikit-learn, an open-source data analysis toolkit, is considered to be the pinnacle of machine learning (ML). Important ideas and traits include: algorithms for making decisions, such as: Identifying and classifying data based on patterns is called classification.
What is cross-validation in sklearn?
Cross-validation is a crucial machine learning concept that benefits data scientists in two essential ways: it can reduce the amount of data needed and makes sure the artificial intelligence model is reliable.
What is model_selection in sklearn?
Model selection is the process of selecting one final machine learning model from among a collection of candidate machine learning models for a training dataset. Model selection is a process that can be applied both across different types of models (e.g. logistic regression, SVM, KNN, etc.)
In this short article, we learned how we can solve ImportError: No module namedsklearn.cross_validation error using various methods. We discussed three different methods to get rid of ImportError: No module namedsklearn.cross_validation error. Moreover, we also discussed how to understand the errors in Python.
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