Import Issue in Gaussian naive bayes example about python

[zaynaab]

Thank you. It worked !

…

On Thu, 18 Apr 2024 at 14:40, Seyyed Reza Moslemi ***@***.***> wrote: You can use this code: `# Gaussian Naive Bayes Example import time from matplotlib import pyplot as plt from sklearn.datasets import load_iris from sklearn.metrics import accuracy_score, ConfusionMatrixDisplay, confusion_matrix from sklearn.model_selection import train_test_split from sklearn.naive_bayes import GaussianNB def main(): """ Gaussian Naive Bayes Example using sklearn function. Iris type dataset is used to demonstrate algorithm. """ # Load Iris dataset iris = load_iris() # Split dataset into train and test data x = iris["data"] # features y = iris["target"] x_train, x_test, y_train, y_test = train_test_split( x, y, test_size=0.3, random_state=1 ) # Gaussian Naive Bayes nb_model = GaussianNB() time.sleep(2.9) model_fit = nb_model.fit(x_train, y_train) y_pred = model_fit.predict(x_test) # Predictions on the test set # Display Confusion Matrix cm = confusion_matrix(y_test, y_pred, labels=nb_model.classes_) ConfusionMatrixDisplay.from_estimator(nb_model, x_test, y_test) plt.title("Normalized Confusion Matrix - IRIS Dataset") plt.show() time.sleep(1.8) final_accuracy = 100 * accuracy_score(y_true=y_test, y_pred=y_pred) print(f"The overall accuracy of the model is: {round(final_accuracy, 2)}%") if *name* == "*main*": main()` — Reply to this email directly, view it on GitHub <#11369 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ANTTTQROZVSD2DZEGM2PJR3Y56IKRAVCNFSM6AAAAABGM2IFWOVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDANRTGQ2TIMZXGQ> . You are receiving this because you authored the thread.Message ID: ***@***.***>

[seyyedmsl82]

Because it is deprecated. Try to use sklearn.metrics.ConfusionMatrixDisplay.

Here is its document: ConfusionMatrixDisplay

[seyyedmsl82]

You can use this code:

# Gaussian Naive Bayes Example
import time

from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import accuracy_score, ConfusionMatrixDisplay, confusion_matrix
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB


def main():

    """
    Gaussian Naive Bayes Example using sklearn function.
    Iris type dataset is used to demonstrate algorithm.
    """

    # Load Iris dataset
    iris = load_iris()

    # Split dataset into train and test data
    x = iris["data"]  # features
    y = iris["target"]
    x_train, x_test, y_train, y_test = train_test_split(
        x, y, test_size=0.3, random_state=1
    )

    # Gaussian Naive Bayes
    nb_model = GaussianNB()
    time.sleep(2.9)
    model_fit = nb_model.fit(x_train, y_train)
    y_pred = model_fit.predict(x_test)  # Predictions on the test set

    # Display Confusion Matrix
    cm = confusion_matrix(y_test, y_pred, labels=nb_model.classes_)
    ConfusionMatrixDisplay.from_estimator(nb_model, x_test, y_test)
    plt.title("Normalized Confusion Matrix - IRIS Dataset")
    plt.show()

    time.sleep(1.8)
    final_accuracy = 100 * accuracy_score(y_true=y_test, y_pred=y_pred)
    print(f"The overall accuracy of the model is: {round(final_accuracy, 2)}%")


if __name__ == "__main__":
    main()