浅学sklearn库之各分类算法实践

各分类算法：

KNN

from sklearn.neighbors import KNeighborsClassifier
import numpy as np


def KNN(X,y,XX)：#X,y 分别为训练数据集的数据和标签，XX为测试数据
    model = KNeighborsClassifier(n_neighbors=10)#默认为5
    model.fit(X,y)

    predicted = model.predict(XX)
    return predicted

SVM

from sklearn.svm import SVC

def SVM(X,y,XX):
    
    model = SVC(c=5.0)
    model.fit(X,y)

    predicted = model.predict(XX)
    return predicted

LR

from sklearn.linear_model import LogisticRegression

def LR(X,y，XX):
    
    model = LogisticRegression()
    model.fit(X,y)

    predicted = model.predict(XX)
    return predicted

决策树

from sklearn.tree import DecisionTreeClassifier

def CTRA(X,y,XX):
    model = DecisionTreeClassifier()
    model.fit(X,y)

    predicted = model.predict(XX)
    return predicted
   

朴素贝叶斯：一个是基于高斯分布求概率，一个是基于多项式分布求概率。

from sklearn.naive_bayes import GaussianNB
from sklearn.naive_bayes import MultinomialNB

def GNB(X,y,XX):
    

    model =GaussianNB()
    model.fit(X,y)
    
    predicted = model.predict(XX)
    return predicted

def MNB(X,y,XX):
    
    model = MultinomialNB()
    model.fit(X,y)

    predicted = model.predict(XX
    return predicted