bayes_classification_model(Python)

最新推荐文章于 2020-11-30 20:06:54 发布

原创最新推荐文章于 2020-11-30 20:06:54 发布 · 268 阅读

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#machine learning #bayes

本文使用Python的sklearn库实现基于鸢尾花数据集的朴素贝叶斯分类器。通过划分训练集与测试集，利用多项式朴素贝叶斯模型进行训练，并评估模型在训练集及测试集上的表现。

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import numpy as np
import pandas as pd
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import classification_report,confusion_matrix

data = datasets.load_iris()
x,y = data.data,data.target

x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.4,random_state=1)

clf = MultinomialNB(alpha=1.0,fit_prior=True,class_prior=None)
clf.fit(x_train,y_train)
print('训练集上预测结果为:\n',classification_report(y_train,clf.predict(x_train)))
trainMatrix = confusion_matrix(y_train,clf.predict(x_train))
dfTrain = pd.DataFrame(trainMatrix)
dfTrain.index.name = 'actual'
dfTrain.columns.name = 'predict'
print(dfTrain)

print('测试集上的预测结果为:\n',classification_report(y_test,clf.predict(x_test),target_names=['a','b','c']))
testMatrix = confusion_matrix(y_test,clf.predict(x_test))
dfTest = pd.DataFrame(testMatrix)
dfTest.index.name = 'actual'
dfTest.columns.name = 'predict'
print(dfTest)