The modulenotfounderror: no module named ‘sklearn.metrics.classification’ occurs only because of incorrect syntax ( wrong way of importing ). Sometimes this error can also replicate in the code if the scikit-learn package is not available in the system. Well, In this article, we will explore both scenarios in detail.
Modulenotfounderror: no module named ‘sklearn.metrics.classification’ ( Cause and Solution ) –
As I explained, In the starting the bad syntax is one of the root causes of this error. The second one is scikit-learn or sklearn package unavailability.
Case 1: Incorrect importing statement –
See sometimes the statement we write, while importing any python module may be incorrect. Most commonly if we do the same importing we will surely get the error.
Wrongly Import :
The screenshot is here-
Correct Import :
Firstly, Let’s jump into the command directly.
from sklearn.metrics import classification_report
Here you have seen the difference. We are first importing sklearn.metrics and then the classification_report package. This will not throw any error. This is not the case with the above import actually it is a generic Importerror error in development.
Case 2: sklearn is not installed or misconfigured –
Most Importantly In this scenario, even after the proper way of importing, Interpreter will the though the same error. There are two possible scenarios, If you have not installed sklearn library or the second one is if you do not configure the path properly. Let’s suppose you installed the package in some virtual environment and running into the different virtual environment. The interpreter will throw this error. You may follow any of the commands to install sklearn. The first one is via the pip package manager and the second one is conda package manager.
pip install -U scikit-learn
Here is the conda command to install scikit-learn (sklearn ).
conda install -c anaconda scikit-learn
Hope these two methods will solve the error for you. In case still stuck in “no module named sklearn.metrics.classification”, please comment in the comment box.
Data Science Learner Team
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