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Naive Bayes with scikit-learn

Variance Setting

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Note: This task assumes the use of scikit-learn version 1.2.2.

Use the iris dataset from the scikit-learn library. Create a GaussianNB model and specify the small value added to the variance of each feature to be 3. Train the model. Then, take the first variance value feature of the first class, which you can obtain using the attribute model.var_[0][0]. This value should be your answer.

Use the code below to start your pipeline.

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB


X, y = load_iris(return_X_y = True)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=2)

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