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45 lines
1.0 KiB
Python
45 lines
1.0 KiB
Python
# Gaussian Naive Bayes Example
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from matplotlib import pyplot as plt
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from sklearn.datasets import load_iris
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from sklearn.metrics import plot_confusion_matrix
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from sklearn.model_selection import train_test_split
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from sklearn.naive_bayes import GaussianNB
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def main():
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"""
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Gaussian Naive Bayes Example using sklearn function.
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Iris type dataset is used to demonstrate algorithm.
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"""
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# Load Iris dataset
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iris = load_iris()
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# Split dataset into train and test data
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X = iris["data"] # features
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Y = iris["target"]
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x_train, x_test, y_train, y_test = train_test_split(
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X, Y, test_size=0.3, random_state=1
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)
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# Gaussian Naive Bayes
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NB_model = GaussianNB()
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NB_model.fit(x_train, y_train)
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# Display Confusion Matrix
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plot_confusion_matrix(
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NB_model,
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x_test,
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y_test,
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display_labels=iris["target_names"],
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cmap="Blues",
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normalize="true",
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)
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plt.title("Normalized Confusion Matrix - IRIS Dataset")
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plt.show()
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if __name__ == "__main__":
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main()
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