Cannot import name ‘plot_confusion_matrix’ from ‘sklearn.metrics’

When you encounter the error message “cannot import name ‘plot_confusion_matrix’ from ‘sklearn.metrics’,” it typically means that the version of scikit-learn library you are using does not have the ‘plot_confusion_matrix’ function.

This function was introduced in scikit-learn version 0.22, so if you are using an older version, you will encounter this error.

To resolve this issue, you have a few options:

1. Upgrade scikit-learn:

The recommended solution is to upgrade your scikit-learn library to the latest version. You can do this by running the following command in your terminal:

pip install --upgrade scikit-learn

Make sure to use a version equal to or higher than 0.22.

2. Check scikit-learn version:

If you are unsure of your scikit-learn version, you can check it programmatically. Run the following code snippet:

import sklearn
print(sklearn.__version__)

This will print out the version of scikit-learn installed in your environment.

3. Using alternative approach:

If upgrading is not an option, you can try using an alternative approach to visualize the confusion matrix. Instead of using the ‘plot_confusion_matrix’ function, you can use the ‘confusion_matrix’ function to compute the confusion matrix, and then display it using matplotlib or any other visualization library.

Here’s an example:

import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix

# Assuming you have true_labels and predicted_labels
cm = confusion_matrix(true_labels, predicted_labels)

# Displaying the confusion matrix
plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)
plt.title('Confusion Matrix')
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
plt.ylabel('True Label')
plt.xlabel('Predicted Label')
plt.show()

In this example, we import ‘confusion_matrix’ from ‘sklearn.metrics’, compute the confusion matrix using true labels and predicted labels, and then use matplotlib to display it.

Remember to replace ‘true_labels’, ‘predicted_labels’, and ‘classes’ with your own variables. You may need to adjust the code depending on the specifics of your problem.