: cannot import name 'plot_confusion_matrix' from 'sklearn.metrics'这个错误怎么解决

这个错误可能是因为您的 scikit-learn 版本太旧，缺少了该函数的实现。您可以尝试更新 scikit-learn 到最新版本来解决这个问题。

您可以使用以下命令来更新 scikit-learn：

pip install --upgrade scikit-learn

如果您已经使用了上述命令更新了 scikit-learn，但仍然遇到该错误，请检查您的代码中是否有其他导入错误或名称冲突。您还可以尝试使用以下命令来重新安装 scikit-learn：

pip uninstall scikit-learn
pip install scikit-learn

如果上述方法仍然无法解决问题，请尝试使用其他版本的 scikit-learn 或联系 scikit-learn 社区获得更好的支持。

相关问题

cannot import name plot_confusion_matrix from sklearn.metrics

The error "cannot import name plot_confusion_matrix from sklearn.metrics" usually occurs when the version of scikit-learn you are using is outdated and does not support the plot_confusion_matrix function. This function was introduced in version 0.22, so you need to make sure you are using at least that version.

You can check the version of scikit-learn you are using with the following code:

import sklearn
print(sklearn.__version__)

If your version is lower than 0.22, you can upgrade it by running the following command in your terminal:

pip install --upgrade scikit-learn

Once you have upgraded scikit-learn, you should be able to import the plot_confusion_matrix function without any issues.

ImportError: cannot import name 'plot_confusion_matrix' from 'sklearn.metrics'怎么解决

这个错误可能是因为你的 scikit-learn 版本过低，没有包含 plot_confusion_matrix 函数。你可以尝试更新 scikit-learn 到最新版本，或者使用以下代码导入 plot_confusion_matrix 函数：

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

def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):
    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
        print("Normalized confusion matrix")
    else:
        print('Confusion matrix, without normalization')

    print(cm)

    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, classes, rotation=45)
    plt.yticks(tick_marks, classes)

    fmt = '.2f' if normalize else 'd'
    thresh = cm.max() / 2.
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, format(cm[i, j], fmt),
                 horizontalalignment="center",
                 color="white" if cm[i, j] > thresh else "black")

    plt.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predicted label')
    plt.show()

这段代码实现了与 scikit-learn 中的 plot_confusion_matrix 函数相同的功能，你可以在需要使用该函数时调用它。