ML02 LogisticRegression

ML实战：逻辑回归+多分类

• 本次实验采用的数据集是sklearn内置的莺尾花数据集

代码实现

import numpy as np
from sklearn import datasets
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
import sys

#初始化数据集
x=datasets.load_iris().data
y=datasets.load_iris().target
x_train,x_test,y_train,y_test=train_test_split(x,y,random_state=6)

X = np.arange(1, len(y_test) + 1)

transfer=StandardScaler()
x_train=transfer.fit_transform(x_train)
x_test=transfer.fit_transform(x_test)
#标准化输入

logistic=LogisticRegression()
logistic.fit(x_train,y_train)
y_predict=logistic.predict(x_test)

#画图输出结果
plt.figure(figsize=(15,8),dpi=80)
plt.scatter(X,y_test,label='real',marker='s',color='blue')
plt.scatter(X,y_predict,label='predict',marker='x',color='red')
plt.legend(loc=[1, 0])
#plt.savefig('E:/Python/ml/pic/LogisticRegression.png')

sys.exit(0)

结果