标签：datasets selection fit div odi ann str info 使用
1.使用朴素贝叶斯模型对iris数据集进行花分类
尝试使用3种不同类型的朴素贝叶斯：
高斯分布型
from sklearn import datasets iris = datasets.load_iris() from sklearn.naive_bayes import GaussianNB gnb = GaussianNB() pred = gnb.fit(iris.data, iris.target) y_pred = pred.predict(iris.data) print(iris.data.shape[0],(iris.target !=y_pred).sum())
多项式型
from sklearn import datasets iris = datasets.load_iris() from sklearn.naive_bayes import MultinomialNB gnb = MultinomialNB() pred = gnb.fit(iris.data, iris.target) y_pred = pred.predict(iris.data) print(iris.data.shape[0],(iris.target !=y_pred).sum())
伯努利型
from sklearn import datasets iris = datasets.load_iris() from sklearn.naive_bayes import BernoulliNB gnb = BernoulliNB() pred = gnb.fit(iris.data, iris.target) y_pred = pred.predict(iris.data) print(iris.data.shape[0],(iris.target !=y_pred).sum())
2.使用sklearn.model_selection.cross_val_score()，对模型进行验证。
from sklearn.naive_bayes import GaussianNB from sklearn.model_selection import cross_val_score gnb = GaussianNB() scores=cross_val_score(gnb, iris.data,iris.target, cv=10) print("Accuracy:%.3f"%scores.mean())
from sklearn.naive_bayes import BernoulliNB from sklearn.model_selection import cross_val_score gnb = BernoulliNB() scores=cross_val_score(gnb, iris.data,iris.target, cv=10) print("Accuracy:%.3f"%scores.mean())
from sklearn.naive_bayes import MultinomialNB from sklearn.model_selection import cross_val_score gnb = MultinomialNB() scores=cross_val_score(gnb, iris.data,iris.target, cv=10) print("Accuracy:%.3f"%scores.mean())
3. 垃圾邮件分类
数据准备：
import csv file_path=r‘C:\Users\Administrator\Desktop\SMSSpamCollectionjsn.txt‘ sms=open(file_path,‘r‘,encoding=‘utf-8‘) sms_data=[] sms_label=[] csv_reader=csv.reader(sms,delimiter=‘\t‘) for line in csv_reader: sms_label.append(line[0]) sms.close() print(len(sms_label)) sms_label
标签：datasets selection fit div odi ann str info 使用
原文地址：https://www.cnblogs.com/844115-l/p/9999240.html
