sklearn逻辑回归实战
题目要求
根据学生两门课的成绩和是否入学的数据，预测学生能否顺利入学：利用ex2data1.txt和ex2data2.txt中的数据，进行逻辑回归和预测。
数据放在最后边。
ex2data1.txt处理
作散点图可知，决策大致符合线性关系，但还是有弯曲（非线性），用线性效果并不好，因此可用两种方案：方案一，无多项式特征；方案二，有多项式特征。
方案一：无多项式特征
对ex2data1.txt中的数据进行逻辑回归，无多项式特征
代码实现如下：
"""
对ex2data1.txt中的数据进行逻辑回归（无多项式特征）
"""
from sklearn.model_selection import train_test_split
from matplotlib.colors import ListedColormap
from sklearn.linear_model import LogisticRegression
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
# 数据格式：成绩1,成绩2,是否被录取（1代表被录取，0代表未被录取）
# 函数（画决策边界）定义
def plot_decision_boundary(model, axis):
x0, x1 = np.meshgrid(
np.linspace(axis[0], axis[1], int((axis[1] - axis[0]) * 100)).reshape(-1, 1),
np.linspace(axis[2], axis[3], int((axis[3] - axis[2]) * 100)).reshape(-1, 1),
)
X_new = np.c_[x0.ravel(), x1.ravel()]
y_predict = model.predict(X_new)
zz = y_predict.reshape(x0.shape)
custom_cmap = ListedColormap(['#EF9A9A', '#FFF59D', '#90CAF9'])
plt.contourf(x0, x1, zz, cmap=custom_cmap)
# 读取数据
data = np.loadtxt('ex2data1.txt', delimiter=',')
data_X = data[:, 0:2]
data_y = data[:, 2]
# 数据分割
X_train, X_test, y_train, y_test = train_test_split(data_X, data_y, random_state=666)
# 训练模型
log_reg = LogisticRegression()
log_reg.fit(X_train, y_train)
# 结果可视化
plot_decision_boundary(log_reg, axis=[0, 100, 0, 100])
plt.scatter(data_X[data_y == 0, 0], data_X[data_y == 0, 1], color='red')
plt.scatter(data_X[data_y == 1, 0], data_X[data_y == 1, 1], color='blue')
plt.xlabel('成绩1')
plt.ylabel('成绩2')
plt.title('两门课程成绩与是否录取的关系')
plt.show()
# 模型测试
print(log_reg.score(X_train, y_train))
print(log_reg.score(X_test, y_test))
输出结果如下：
0.8533333333333334
0.76
方案二：引入多项式特征
对ex2data1.txt中的数据进行逻辑回归，引入多项式特征。经调试，当degree为3时，耗费时间较长；当degree为2时，耗费时间可接受，效果与方案一相比好了很多
实现如下：
"""
对ex2data1.txt中的数据进行逻辑回归（引入多项式特征）
"""
from sklearn.model_selection import train_test_split
from matplotlib.colors import ListedColormap
from sklearn.linear_model import LogisticRegression
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import PolynomialFeatures
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
# 数据格式：成绩1,成绩2,是否被录取（1代表被录取，0代表未被录取）
# 函数定义
def plot_decision_boundary(model, axis):
x0, x1 = np.meshgrid(
np.linspace(axis[0], axis[1], int((axis[1] - axis[0]) * 100)).reshape(-1, 1),
np.linspace(axis[2], axis[3], int((axis[3] - axis[2]) * 100)).reshape(-1, 1),
)
X_new = np.c_[x0.ravel(), x1.ravel()]
y_predict = model.predict(X_new)
zz = y_predict.reshape(x0.shape)
custom_cmap = ListedColormap(['#EF9A9A', '#FFF59D', '#90CAF9'])
plt.contourf(x0, x1, zz, cmap=custom_cmap)
def PolynomialLogisticRegression(degree):
return Pipeline([
('poly', PolynomialFeatures(degree=degree)),
('std_scaler', StandardScaler()),
('log_reg', LogisticRegression())
])
# 读取数据
data = np.loadtxt('ex2data1.txt', delimiter=',')
data_X = data[:, 0:2]
data_y = data[:, 2]
# 数据分割
X_train, X_test, y_train, y_test = train_test_split(data_X, data_y, random_state=666)
# 训练模型
poly_log_reg = PolynomialLogisticRegression(degree=2)
poly_log_reg.fit(X_train, y_train)
# 结果可视化
plot_decision_boundary(poly_log_reg, axis=[0, 100, 0, 100])
plt.scatter(data_X[data_y == 0, 0], data_X[data_y == 0, 1], color='red')
plt.scatter(data_X[data_y == 1, 0], data_X[data_y == 1, 1], color='blue')
plt.xlabel('成绩1')
plt.ylabel('成绩2')
plt.title('两门课程成绩与是否录取的关系')
plt.show()
# 模型测试
print(poly_log_reg.score(X_train, y_train))
print(poly_log_reg.score(X_test, y_test))
输出如下：
0.92
0.92
ex2data2.txt处理
作散点图可知，这组数据的决策边界绝对是非线性的，所以直接引入多项式特征对ex2data2.txt中的数据进行逻辑回归。
代码实现如下：
"""
对ex2data2.txt中的数据进行逻辑回归（引入多项式特征）
"""
from sklearn.model_selection import train_test_split
from matplotlib.colors import ListedColormap
from sklearn.linear_model import LogisticRegression
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import PolynomialFeatures
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
# 数据格式：成绩1,成绩2,是否被录取（1代表被录取，0代表未被录取）
# 函数定义
def plot_decision_boundary(model, axis):
x0, x1 = np.meshgrid(
np.linspace(axis[0], axis[1], int((axis[1] - axis[0]) * 100)).reshape(-1, 1),
np.linspace(axis[2], axis[3], int((axis[3] - axis[2]) * 100)).reshape(-1, 1),
)
X_new = np.c_[x0.ravel(), x1.ravel()]
y_predict = model.predict(X_new)
zz = y_predict.reshape(x0.shape)
custom_cmap = ListedColormap(['#EF9A9A', '#FFF59D', '#90CAF9'])
plt.contourf(x0, x1, zz, cmap=custom_cmap)
def PolynomialLogisticRegression(degree):
return Pipeline([
('poly', PolynomialFeatures(degree=degree)),
('std_scaler', StandardScaler()),
('log_reg', LogisticRegression())
])
# 读取数据
data = np.loadtxt('ex2data2.txt', delimiter=',')
data_X = data[:, 0:2]
data_y = data[:, 2]
# 数据分割
X_train, X_test, y_train, y_test = train_test_split(data_X, data_y, random_state=666)
# 训练模型
poly_log_reg = PolynomialLogisticRegression(degree=2)
poly_log_reg.fit(X_train, y_train)
# 结果可视化
plot_decision_boundary(poly_log_reg, axis=[-1, 1, -1, 1])
plt.scatter(data_X[data_y == 0, 0], data_X[data_y == 0, 1], color='red')
plt.scatter(data_X[data_y == 1, 0], data_X[data_y == 1, 1], color='blue')
plt.xlabel('成绩1')
plt.ylabel('成绩2')
plt.title('两门课程成绩与是否录取的关系')
plt.show()
# 模型测试
print(poly_log_reg.score(X_train, y_train))
print(poly_log_reg.score(X_test, y_test))
输出结果如下：
由图可知，分类结果较好。
0.7954545454545454
0.9
两份数据
ex2data1.txt
34.62365962451697,78.0246928153624,0
30.28671076822607,43.89499752400101,0
35.84740876993872,72.90219802708364,0
60.18259938620976,86.30855209546826,1
79.0327360507101,75.3443764369103,1
45.08327747668339,56.3163717815305,0
61.10666453684766,96.51142588489624,1
75.02474556738889,46.55401354116538,1
76.09878670226257,87.42056971926803,1
84.43281996120035,43.53339331072109,1
95.86155507093572,38.22527805795094,0
75.01365838958247,30.60326323428011,0
82.30705337399482,76.48196330235604,1
69.36458875970939,97.71869196188608,1
39.53833914367223,76.03681085115882,0
53.9710521485623,89.20735013750205,1
69.07014406283025,52.74046973016765,1
67.94685547711617,46.67857410673128,0
70.66150955499435,92.92713789364831,1
76.97878372747498,47.57596364975532,1
67.37202754570876,42.83843832029179,0
89.67677575072079,65.79936592745237,1
50.534788289883,48.85581152764205,0
34.21206097786789,44.20952859866288,0
77.9240914545704,68.9723599933059,1
62.27101367004632,69.95445795447587,1
80.1901807509566,44.82162893218353,1
93.114388797442,38.80067033713209,0
61.83020602312595,50.25610789244621,0
38.78580379679423,64.99568095539578,0
61.379289447425,72.80788731317097,1
85.40451939411645,57.05198397627122,1
52.10797973193984,63.12762376881715,0
52.04540476831827,69.43286012045222,1
40.23689373545111,71.16774802184875,0
54.63510555424817,52.21388588061123,0
33.91550010906887,98.86943574220611,0
64.17698887494485,80.90806058670817,1
74.78925295941542,41.57341522824434,0
34.1836400264419,75.2377203360134,0
83.90239366249155,56.30804621605327,1
51.54772026906181,46.85629026349976,0
94.44336776917852,65.56892160559052,1
82.36875375713919,40.61825515970618,0
51.04775177128865,45.82270145776001,0
62.22267576120188,52.06099194836679,0
77.19303492601364,70.45820000180959,1
97.77159928000232,86.7278223300282,1
62.07306379667647,96.76882412413983,1
91.56497449807442,88.69629254546599,1
79.94481794066932,74.16311935043758,1
99.2725269292572,60.99903099844988,1
90.54671411399852,43.39060180650027,1
34.52451385320009,60.39634245837173,0
50.2864961189907,49.80453881323059,0
49.58667721632031,59.80895099453265,0
97.64563396007767,68.86157272420604,1
32.57720016809309,95.59854761387875,0
74.24869136721598,69.82457122657193,1
71.79646205863379,78.45356224515052,1
75.3956114656803,85.75993667331619,1
35.28611281526193,47.02051394723416,0
56.25381749711624,39.26147251058019,0
30.05882244669796,49.59297386723685,0
44.66826172480893,66.45008614558913,0
66.56089447242954,41.09209807936973,0
40.45755098375164,97.53518548909936,1
49.07256321908844,51.88321182073966,0
80.27957401466998,92.11606081344084,1
66.74671856944039,60.99139402740988,1
32.72283304060323,43.30717306430063,0
64.0393204150601,78.03168802018232,1
72.34649422579923,96.22759296761404,1
60.45788573918959,73.09499809758037,1
58.84095621726802,75.85844831279042,1
99.82785779692128,72.36925193383885,1
47.26426910848174,88.47586499559782,1
50.45815980285988,75.80985952982456,1
60.45555629271532,42.50840943572217,0
82.22666157785568,42.71987853716458,0
88.9138964166533,69.80378889835472,1
94.83450672430196,45.69430680250754,1
67.31925746917527,66.58935317747915,1
57.23870631569862,59.51428198012956,1
80.36675600171273,90.96014789746954,1
68.46852178591112,85.59430710452014,1
42.0754545384731,78.84478600148043,0
75.47770200533905,90.42453899753964,1
78.63542434898018,96.64742716885644,1
52.34800398794107,60.76950525602592,0
94.09433112516793,77.15910509073893,1
90.44855097096364,87.50879176484702,1
55.48216114069585,35.57070347228866,0
74.49269241843041,84.84513684930135,1
89.84580670720979,45.35828361091658,1
83.48916274498238,48.38028579728175,1
42.2617008099817,87.10385094025457,1
99.31500880510394,68.77540947206617,1
55.34001756003703,64.9319380069486,1
74.77589300092767,89.52981289513276,1
ex2data2.txt
0.051267,0.69956,1
-0.092742,0.68494,1
-0.21371,0.69225,1
-0.375,0.50219,1
-0.51325,0.46564,1
-0.52477,0.2098,1
-0.39804,0.034357,1
-0.30588,-0.19225,1
0.016705,-0.40424,1
0.13191,-0.51389,1
0.38537,-0.56506,1
0.52938,-0.5212,1
0.63882,-0.24342,1
0.73675,-0.18494,1
0.54666,0.48757,1
0.322,0.5826,1
0.16647,0.53874,1
-0.046659,0.81652,1
-0.17339,0.69956,1
-0.47869,0.63377,1
-0.60541,0.59722,1
-0.62846,0.33406,1
-0.59389,0.005117,1
-0.42108,-0.27266,1
-0.11578,-0.39693,1
0.20104,-0.60161,1
0.46601,-0.53582,1
0.67339,-0.53582,1
-0.13882,0.54605,1
-0.29435,0.77997,1
-0.26555,0.96272,1
-0.16187,0.8019,1
-0.17339,0.64839,1
-0.28283,0.47295,1
-0.36348,0.31213,1
-0.30012,0.027047,1
-0.23675,-0.21418,1
-0.06394,-0.18494,1
0.062788,-0.16301,1
0.22984,-0.41155,1
0.2932,-0.2288,1
0.48329,-0.18494,1
0.64459,-0.14108,1
0.46025,0.012427,1
0.6273,0.15863,1
0.57546,0.26827,1
0.72523,0.44371,1
0.22408,0.52412,1
0.44297,0.67032,1
0.322,0.69225,1
0.13767,0.57529,1
-0.0063364,0.39985,1
-0.092742,0.55336,1
-0.20795,0.35599,1
-0.20795,0.17325,1
-0.43836,0.21711,1
-0.21947,-0.016813,1
-0.13882,-0.27266,1
0.18376,0.93348,0
0.22408,0.77997,0
0.29896,0.61915,0
0.50634,0.75804,0
0.61578,0.7288,0
0.60426,0.59722,0
0.76555,0.50219,0
0.92684,0.3633,0
0.82316,0.27558,0
0.96141,0.085526,0
0.93836,0.012427,0
0.86348,-0.082602,0
0.89804,-0.20687,0
0.85196,-0.36769,0
0.82892,-0.5212,0
0.79435,-0.55775,0
0.59274,-0.7405,0
0.51786,-0.5943,0
0.46601,-0.41886,0
0.35081,-0.57968,0
0.28744,-0.76974,0
0.085829,-0.75512,0
0.14919,-0.57968,0
-0.13306,-0.4481,0
-0.40956,-0.41155,0
-0.39228,-0.25804,0
-0.74366,-0.25804,0
-0.69758,0.041667,0
-0.75518,0.2902,0
-0.69758,0.68494,0
-0.4038,0.70687,0
-0.38076,0.91886,0
-0.50749,0.90424,0
-0.54781,0.70687,0
0.10311,0.77997,0
0.057028,0.91886,0
-0.10426,0.99196,0
-0.081221,1.1089,0
0.28744,1.087,0
0.39689,0.82383,0
0.63882,0.88962,0
0.82316,0.66301,0
0.67339,0.64108,0
1.0709,0.10015,0
-0.046659,-0.57968,0
-0.23675,-0.63816,0
-0.15035,-0.36769,0
-0.49021,-0.3019,0
-0.46717,-0.13377,0
-0.28859,-0.060673,0
-0.61118,-0.067982,0
-0.66302,-0.21418,0
-0.59965,-0.41886,0
-0.72638,-0.082602,0
-0.83007,0.31213,0
-0.72062,0.53874,0
-0.59389,0.49488,0
-0.48445,0.99927,0
-0.0063364,0.99927,0
0.63265,-0.030612,0
作者：@臭咸鱼
转载请注明出处：https://www.cnblogs.com/chouxianyu/
欢迎讨论和交流!
sklearn逻辑回归实战的更多相关文章
- 通俗地说逻辑回归【Logistic regression】算法（二）sklearn逻辑回归实战
前情提要: 通俗地说逻辑回归[Logistic regression]算法(一) 逻辑回归模型原理介绍 上一篇主要介绍了逻辑回归中,相对理论化的知识,这次主要是对上篇做一点点补充,以及介绍sklear ...
- sklearn逻辑回归(Logistic Regression,LR)调参指南
python信用评分卡建模(附代码,博主录制) https://study.163.com/course/introduction.htm?courseId=1005214003&utm_ca ...
- sklearn逻辑回归
sklearn逻辑回归 logistics回归名字虽然叫回归,但实际是用回归方法解决分类的问题,其形式简洁明了,训练的模型参数还有实际的解释意义,因此在机器学习中非常常见. 理论部分 设数据集有n个独 ...
- sklearn逻辑回归(Logistic Regression)类库总结
class sklearn.linear_model.LogisticRegression(penalty=’l2’, dual=False, tol=0.0001, C=1.0, fit_inter ...
- sklearn逻辑回归库函数直接拟合数据
from sklearn import model_selection from sklearn.linear_model import LogisticRegression from sklearn ...
- 机器学习入门-概率阈值的逻辑回归对准确度和召回率的影响 lr.predict_proba(获得预测样本的概率值)
1.lr.predict_proba(under_text_x) 获得的是正负的概率值 在sklearn逻辑回归的计算过程中,使用的是大于0.5的是正值,小于0.5的是负值,我们使用使用不同的概率结 ...
- 逻辑回归原理_挑战者飞船事故和乳腺癌案例_Python和R_信用评分卡（AAA推荐）
sklearn实战-乳腺癌细胞数据挖掘(博客主亲自录制视频教程) https://study.163.com/course/introduction.htm?courseId=1005269003&a ...
- 逻辑回归--美国挑战者号飞船事故_同盾分数与多头借贷Python建模实战
python信用评分卡(附代码,博主录制) https://study.163.com/course/introduction.htm?courseId=1005214003&utm_camp ...
- 机器学习_线性回归和逻辑回归_案例实战：Python实现逻辑回归与梯度下降策略_项目实战：使用逻辑回归判断信用卡欺诈检测
线性回归: 注:为偏置项,这一项的x的值假设为[1,1,1,1,1....] 注:为使似然函数越大,则需要最小二乘法函数越小越好 线性回归中为什么选用平方和作为误差函数?假设模型结果与测量值 误差满足 ...
随机推荐
- Andrew Ng机器学习课程16
Andrew Ng机器学习课程16 声明:引用请注明出处http://blog.csdn.net/lg1259156776/ 说明:主要介绍了ICA算法,并利用最大似然估计和随机梯度上升算法进行求解, ...
- php csv 简单的导入
if($act == 'user_upload_do'){ global $db; $filename = $_FILES['file']['tmp_name']; if (empty ($filen ...
- 高级UI-Snackbar
在与用户的交互中,最为常用的Toast和Dialog,但二者都存在其局限,Toast无法与用户进行交互,Dialog虽然可以与用户交互,但却会阻断用户操作的连贯性,介于二者之间的平衡,Snackbar ...
- 日常工作问题解决：centos7下使用yum安装软件报yum.pid锁定
问题描述: 在centos7下使用yum进行软件安装时报yum.pid已经被锁定,如下所示: [root@centos7-129 ~]# yum -y install dhcp 已加载插件:faste ...
- c# 中对于每次修改的程序 都必须重新手动生成 才能编译的问题
问题描述:原来用VS2017,升级了VS2019,发现修改了Winform界面,F5运行竟然还是原来的界面 问题解决: 需要修改两个地方 工具>> 选项>> 项目和解决方案&g ...
- Hadoop的eclipse的插件是怎么安装的？
[学习笔记] 1)网上下载hadoop-eclipse-plugin-2.7.4.jar,将该jar包拷贝到Eclipse安装目录下的dropins文件夹下,我的目录是C:\Users\test\ec ...
- 关于db2中listagg函数开发中的体验
一.首先解释一下可能会查询的基础问题: 1.1db2 “with ur”是什么意思: 在DB2中,共有四种隔离级:RS,RR,CS,UR.以下对四种隔离级进行一些描述,同时附上个人做试验的结果.隔离级 ...
- Scrapy各部分运行机制?Xpath为None？多层Response如何编写？搞定Scrapy的坑
前言 Scrapy那么多模块都是怎么结合的啊?明明在chrome上的xpath helper插件写好了xpath,为什么到程序就读取的是None?Scrapy可以直接写多层response么?难道必须 ...
- 关于typecho发布文章后的错位
今天发布了一篇文章,发布后发现,what?主页错位了,安装控制变量法知道,肯定是这篇文章有什么不可告人的秘密. 所以,顺便使用一下二分法查找一下为啥,最后找到是因为使用了---------->( ...
- Struts2简介、初步使用
今日分享的是楼楼新学的一个框架,Struts2: 一:Struts2简介: Struts2是一个基于MVC设计模式的Web应用框架,它本质上相当于一个servlet,在MVC设计模式中,Struts2 ...