1.朴素贝叶斯
朴素贝叶斯参考链接:https://blog.csdn.net/qq_27009517/article/details/80044431
代码样例:
import numpy as np
from sklearn import datasets
from sklearn.naive_bayes import GaussianNB
from sklearn.naive_bayes import BernoulliNB
from sklearn.naive_bayes import MultinomialNB
# 高斯模型
iris = datasets.load_iris()
clf = GaussianNB()
clf.fit(iris.data, iris.target)
print(clf.predict(iris.data[2]))
# 多项式模型
X = np.random.randint(5, size=(6, 100))
y = np.array([1, 2, 3, 4, 5, 6])
clf = MultinomialNB()
clf.fit(X, y)
print(clf.predict(X[2]))
# 伯努利模型
X = np.random.randint(2, size=(6, 100))
Y = np.array([1, 2, 3, 4, 4, 5])
clf = BernoulliNB()
clf.fit(X, Y)
print(clf.predict(X[2]))
2.支持向量机
支持向量机参考链接1:https://blog.csdn.net/weixin_39605679/article/details/81170300
支持向量机参考链接2:https://blog.csdn.net/Kaiyuan_sjtu/article/details/80064145
3.LDA主题模型
LDA主题模型参考链接1:https://blog.csdn.net/u013710265/article/details/73480332
LDA主题模型参考链接2:https://blog.csdn.net/Kaiyuan_sjtu/article/details/83572927