支援向量機:

# -*- coding: utf-8 -*-
import sklearn
from sklearn.svm import SVC
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn import datasets
import pandas as pd
import numpy


def getData_1():

    iris = datasets.load_iris()
    X = iris.data   #樣本特徵矩陣，150*4矩陣，每行一個樣本，每個樣本維度是4
 
    y = iris.target #樣本類別矩陣，150維行向量，每個元素代表一個樣本的類別


    df1=pd.DataFrame(X, columns =['SepalLengthCm','SepalWidthCm','PetalLengthCm','PetalWidthCm'])
    df1['target']=y

    return df1

df=getData_1()


X_train, X_test, y_train, y_test = train_test_split(df.iloc[:,0:3],df['target'], test_size=0.3, random_state
 
=42)
print X_train, X_test, y_train, y_test


model = SVC(C=1.0, kernel='rbf', gamma='auto')
"""引數
---
    C：誤差項的懲罰引數C
    gamma: 核相關係數。浮點數，If gamma is ‘auto’ then 1/n_features will be used instead.
"""


model.fit(X_train,y_train)
predict=model.predict(X_test)
print predict
print y_test.values

print 
 
'SVC分類:{:.3f}'.format(model.score(X_test, y_test))

結果：

[1 0 2 1 1 0 1 2 1 1 2 0 0 0 0 1 2 1 1 2 0 2 0 2 2 2 2 2 0 0 0 0 1 0 0 2 1
 0 0 0 2 1 1 0 0]
[1 0 2 1 1 0 1 2 1 1 2 0 0 0 0 1 2 1 1 2 0 2 0 2 2 2 2 2 0 0 0 0 1 0 0 2 1
 0 0 0 2 1 1 0 0]

SVC分類:1.000

準確度驚人的100%......,比線性迴歸和樸素貝葉斯分類高很多。。。