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

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When you encounter the error “cannot import name ‘plot_roc_curve’ from ‘sklearn.metrics'” it means that the function plot_roc_curve is not available in the sklearn.metrics module.

This error can occur when using an older version of scikit-learn that does not include this function. The plot_roc_curve function was introduced in scikit-learn version 0.22.

To resolve this issue, you can take the following steps:

  1. Check your scikit-learn version: You can check the version of scikit-learn installed in your environment by running the following code:
    2. import sklearn
print(sklearn.__version__)

    If the version is below 0.22, you will need to upgrade scikit-learn to a newer version to use plot_roc_curve.

  2. Upgrade scikit-learn: To upgrade scikit-learn, you can use the pip package manager. Open your terminal or command prompt and run the following command:
    3. pip install --upgrade scikit-learn

    This will upgrade scikit-learn to the latest version available.

  3. Verify the availability of plot_roc_curve: After upgrading scikit-learn, you can verify that the plot_roc_curve function is available by running the following code:
    4. from sklearn.metrics import plot_roc_curve
print(plot_roc_curve)

    If no error occurs, it means that the import was successful and you can now use the plot_roc_curve function in your code.

Here’s an example of how you can use the plot_roc_curve function to plot the Receiver Operating Characteristic (ROC) curve:

from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import plot_roc_curve

# Load the breast cancer dataset
data = load_breast_cancer()
X = data.data
y = data.target

# Split the dataset into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create a logistic regression model
model = LogisticRegression()

# Train the model
model.fit(X_train, y_train)

# Plot ROC curve
plot_roc_curve(model, X_test, y_test)

This example demonstrates how to use plot_roc_curve with the breast cancer dataset. It splits the dataset into training and test sets, creates a logistic regression model, trains the model, and finally plots the ROC curve using the plot_roc_curve function.

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