吴裕雄python机器学习——多项式贝叶斯分类器MultinomialNB模型

吴裕雄python机器学习——多项式贝叶斯分类器MultinomialNB
模型
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets,naive_bayes
from sklearn.model_selection import train_test_split
# 加载 scikit-learn ⾃带的 digits 数据集
def load_data():
'''
加载⽤于分类问题的数据集。

这⾥使⽤ scikit-learn ⾃带的 digits 数据集
'''
digits=datasets.load_digits()
return train_test_split(digits.data,digits.target,test_size=0.25,random_state=0,stratify=digits.target)
#多项式贝叶斯分类器MultinomialNB模型
def test_MultinomialNB(*data):
X_train,X_test,y_train,y_test=data
cls=naive_bayes.MultinomialNB()
cls.fit(X_train,y_train)
print('Training Score: %.2f' % cls.score(X_train,y_train))
print('Testing Score: %.2f' % cls.score(X_test, y_test))
# 产⽣⽤于分类问题的数据集
X_train,X_test,y_train,y_test=load_data()
# 调⽤ test_GaussianNB
test_MultinomialNB(X_train,X_test,y_train,y_test)
def test_MultinomialNB_alpha(*data):
'''
测试 MultinomialNB 的预测性能随 alpha 参数的影响
'''
X_train,X_test,y_train,y_test=data
alphas=np.logspace(-2,5,num=200)
train_scores=[]
test_scores=[]
for alpha in alphas:
cls=naive_bayes.MultinomialNB(alpha=alpha)
cls.fit(X_train,y_train)
train_scores.append(cls.score(X_train,y_train))
test_scores.append(cls.score(X_test, y_test))
## 绘图
fig=plt.figure()
ax=fig.add_subplot(1,1,1)
ax.plot(alphas,train_scores,label="Training Score")
ax.plot(alphas,test_scores,label="Testing Score")
ax.set_xlabel(r"$\alpha$")
ax.set_ylabel("score")
ax.set_ylim(0,1.0)
ax.set_title("MultinomialNB")
ax.set_xscale("log")
plt.show()
# 调⽤ test_MultinomialNB_alpha
test_MultinomialNB_alpha(X_train,X_test,y_train,y_test)。