sklearn_logistic_regression

from sklearn.datasets import load_iris   
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np

# 加载数据集
iris = load_iris()

# 数据特征：150行， 4列
features = iris['data']

# 对应的鸢尾花种类: 150个，三种鸢尾花分别用 0,1,2 表示
target = iris['target']

# 自定义4个特征的名称
feature_names = iris.feature_names
feature_names = ['花萼长度', '花萼宽度', '花瓣长度', '花瓣宽度']

# 自定义三种鸢尾花的名称
class_names = iris.target_names
class_names = ['山鸢尾花', '变色鸢尾花', '维吉尼亚鸢尾花']

# 把样本分成训练集和测试集两部分, 两者比例为： 7:3
X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.3, random_state=42)

# 训练
lr = LogisticRegression() 
lr.fit(X_train, y_train)

# 预测
output = lr.predict(X_test) 

# 计算准确率
acc = np.mean(output == y_test)*100
print("The accuracy of the logistic regression classifier is: \t", acc, "%")