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What to expect in this article?
1. What does a module not found error mean?
2. What does the sci-kit-learn library do?
3. What is cross-validation, and when does modulenotfounderror: no module named 'sklearn.cross_validation' occur?
4. [Fixed] modulenotfounderror: no module named 'sklearn.cross_validation'
What does a module not found error mean?
Modules are an essential part of the Python programming language. Modules enable one to write and distribute code in various files. This contributes to a codebase's readability and maintainability.
When Python is unable to locate an error, the ModuleNotFoundError is shown. The most typical reason for this error is failing to install a module or wrongly importing a module. When you fail to install a project dependency, Python doesn't quite know where to look for the dependency because you haven't installed it. If you're working with an external module, double-check that it's installed.
What Does Scikit-Learn Library Do
Scikit-learn is an open-source python library that is used when dealing with data science and machine learning. It is linked to NumPy and SciPy and is regarded as one of the best libraries for extensive data and complex computations. It has practical tools that include an extensive range of algorithms for performing everyday machine learning and data mining functionalities.
The following are applications of the sci-kit-learn library
1. Pre-processing
2. Classification
3. Regression
4. Clustering
5. Dimensionality reduction
6. Model selection
What Is Cross-Validation
Cross-Validation is a statistical method for analyzing and comparing learning algorithms that divide data into two segments: one for learning or training a model and the other for validating the model. Typically, the training and validation sets must cross over in successive cycles so that each data point has a chance of being validated against. k-fold cross-validation is the most basic type of cross-validation. Other types of cross-validation are variations on k-fold cross-validation or include many rounds of k-fold cross-validation.
When Does (modulenotfounderror: no module named 'sklearn.cross_validation') occur?
As discussed earlier, a module not found error may occur when specific modules have not been installed or have been installed wrongly, but for this case, this may not be the reason.
Let's break it down
Although you may have installed the sci-kit-learn library correctly, you may still get this error for one primary reason as mentioned below:
Producing the Error:
The sci-kit-learn library has been undergoing various changes and modifications, one being the cause for this error ( the cross-validation feature). The change is the renaming and deprecation of the cross-validation sub-module into model selection. After version 0.20, the module was changed from cross-validation to model selection while previous versions of 0.18 were using cross-validation module hence when importing cross-validation module instead of model selection, the following error occurs
from sklearn.cross_validation import cross_val_score
Output:
ImportError: No module named sklearn.cross_validation
[Fixed] modulenotfounderror: no module named 'sklearn.cross_validation'
When implementing a data set using Jupyter notebook, use model selection instead of cross-validation, the output has no error as shown below
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn import datasets
from sklearn import svm
X, y = datasets.load_iris(return_X_y=True)
X.shape, y.shape
((150, 4), (150,))
Output
((150, 4), (150,))
From the output above, no error is generated from using ("from sklearn.model_selection import train_test_split") hence the output generates the split of X and y as expected.
Conclusion
When Python is unable to locate an error, the ModuleNotFoundError is shown. The most typical reason for this error is failing to install a module or wrongly importing a module.
Scikit-learn is an open-source python library that is used for performing everyday machine learning and data mining functionalities.
Cross-validation can be used to check the accuracy of models.
(Module not found error) of cross-validation is a result of the name change. Using model selection instead of cross-validation solves the problem.