机器学习之逻辑回归实践：购买意向预测与其他预测

逻辑回归的主要用途有预测（如预测用户购买意向）、判别（如判别某人是否会患胃癌）等。

今天使用逻辑回归做了个购买意向的预测。

数据集如下（共400条数据，4个特征，这里我们不使用ID和性别，只使用年龄和收入两个特征）：

具体实现代码如下：

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
#import matplotlib.pyplot as plt
from sklearn.metrics import accuracy_scoredataset = pd.read_csv('Social_Network_Ads.csv')
X = dataset.iloc[ : ,2:4].values
Y = dataset.iloc[ : ,4].valuesX_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2)sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test )classifier = LogisticRegression()
classifier.fit(X_train, Y_train)y_pred = classifier.predict(X_test)acc = accuracy_score(Y_test, y_pred)
print("准确率为：", acc)

此外还有一个其他方面的预测代码示例如下:

# encoding: utf-8
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
import numpy as np
import pandas as pd
import matplotlib.pyplot as pltdataset = pd.read_csv('dataset.csv', delimiter=',')
X = np.asarray(dataset.get(['x1', 'x2']))
y = np.asarray(dataset.get('y'))# 划分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5)# 使用 sklearn 的 LogisticRegression 作为模型，其中有 penalty，solver，dual 几个比较重要的参数，不同的参数有不同的准确率，这里为了简便都使用默认的，详细的请参考 sklearn 文档
model = LogisticRegression(solver='liblinear')# 拟合
model.fit(X, y)# 预测测试集
predictions = model.predict(X_test)# 打印准确率
print('测试集准确率：', accuracy_score(y_test, predictions))weights = np.column_stack((model.intercept_, model.coef_)).transpose()n = np.shape(X_train)[0]
xcord1 = []
ycord1 = []
xcord2 = []
ycord2 = []
for i in range(n):if int(y_train[i]) == 1:xcord1.append(X_train[i, 0])ycord1.append(X_train[i, 1])else:xcord2.append(X_train[i, 0])ycord2.append(X_train[i, 1])
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(xcord1, ycord1, s=30, c='red', marker='s')
ax.scatter(xcord2, ycord2, s=30, c='green')
x_ = np.arange(-3.0, 3.0, 0.1)
y_ = (-weights[0] - weights[1] * x_) / weights[2]
ax.plot(x_, y_)
plt.xlabel('x1')
plt.ylabel('x2')
plt.show()

该出原文原文链接：https://blog.csdn.net/qq_24671941/article/details/94767008

癌症预测

示例代码如下：

# -*- coding: utf-8 -*-from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import classification_report# 加载数据
breast = load_breast_cancer()# 数据拆分
X_train, X_test, y_train, y_test = train_test_split(breast.data, breast.target)# 数据标准化
std = StandardScaler()
X_train = std.fit_transform(X_train)
X_test = std.transform(X_test)# 训练预测
lg = LogisticRegression()lg.fit(X_train, y_train)y_predict = lg.predict(X_test)# 查看训练准确度和预测报告
print(lg.score(X_test, y_test))
print(classification_report(y_test, y_predict, labels=[0, 1], target_names=["良性", "恶性"]))"""
0.958041958041958precision    recall  f1-score   support良性       0.98      0.90      0.93        48恶性       0.95      0.99      0.97        95avg / total       0.96      0.96      0.96       143"""

该处原文链接为：https://blog.csdn.net/mouday/article/details/86653227