朴素贝叶斯数据分类------

------------------后期会编辑些关于朴素贝叶斯算法的推导及代码分析-----------------
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB, BernoulliNB, MultinomialNB
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import accuracy_scoredata = pd.read_csv('iris.data', header=None)
# print(data.head())X = data.iloc[:, :-1]
Y = data.iloc[:, -1]label = LabelEncoder()
Y = label.fit_transform(Y)x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=10)gaussian = GaussianNB()
bernoull = BernoulliNB()
multin = MultinomialNB()list_A = [gaussian, bernoull, multin]one_test = []
train_score = []for one in list_A:one.fit(x_train, y_train)one_test.append(one.score(x_test, y_test))train_score.append(one.score(x_train, y_train))# one.score(x_train, y_train)# y_hat = one.predict(x_train)#####各种错误# y_hat = one.predict(y_train)# one.score(x_train,y_hat)# one.score(y_train,y_hat)####正确# accuracy_score(y_hat,y_train)
print(one_test)
print('=' * 50)
print(train_score)

E:\myprogram\anaconda\envs\python3.6\python.exe E:/xxxxxx/01_朴素贝叶斯鸢尾花数据分类.py
[1.0, 0.23333333333333334, 0.6]
==================================================
[0.95, 0.35833333333333334, 0.725]

Process finished with exit code 0