sklearn 朴素贝叶斯

1. 朴素贝叶斯 iris案例

from sklearn import datasets
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.svm import SVC
from sklearn.naive_bayes import GaussianNB, BernoulliNB, MultinomialNB

"""
    加载数据
"""
iris = datasets.load_iris()
X_data, y_data = iris.data, iris.target
X_train, X_test, y_train, y_test = train_test_split(X_data, y_data, test_size=0.25)

"""
    特征归一化
"""
scaler = MinMaxScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)


def test(clf):
    """
        训练
    """
    clf.fit(X_train, y_train)
    """
        评估
    """
    print('=' * 60)
    print("train score: ", clf.score(X_train, y_train))
    print("test score: ", clf.score(X_test, y_test))

test(MultinomialNB())   # 多项式模型
test(GaussianNB())      # 高斯模型
test(BernoulliNB())      # Bernoulli模型