逻辑回归模型参数详解

求解对偶问题：如果数据量小但特征很多可以改成true

一般可以设置max_iter 大一些而tol小一些

Class_weight:输入{0:1,1:3}则代表1类样本的每条数据在计算损失函数时都会*3，当输入balanced，则调整为真实样本比例的反比，以达到平衡，但实际情况中不常用

Multi_class:默认情况auto，模型会优先根据惩罚项和solver选择OVR还是MVM

Solver：

逻辑回归调参实验

首先介绍PolynomialFeatures函数，其参数有degree（最高阶数）、interaction_only（是否包含交叉项）、include_bias（是否只包含0阶计算结果、偏置项）

具体实验代码如下：

#!/usr/bin/env python

coding: utf-8

In $1$ :

import numpy as np

import pandas as pd

In $2$ :

import matplotlib as mpl

import matplotlib.pyplot as plt

In $3$ :

from sklearn.preprocessing import StandardScaler

from sklearn.preprocessing import PolynomialFeatures

from sklearn.linear_model import LogisticRegression

from sklearn.pipeline import make_pipeline

In $4$ :

from sklearn.model_selection import train_test_split

from sklearn.metrics import accuracy_score

In $5$ :

np.random.seed(24)

x=np.random.normal(0,1,size=(1000,2))

In $6$ :

y=np.array(x $:,0$ +x $:,1$ **2<1.5,int)

In $7$ :

plt.scatter(x $:,0$ ,x $:,1$ ,c=y)#c表示颜色

In $8$ :

np.random.seed(24)

for i in range(200):

y $np.random.randint(1000)$ =1

y $np.random.randint(1000)$ =0

In $9$ :

plt.scatter(x $:,0$ ,x $:,1$ ,c=y)

In $10$ :

x_train,x_test,y_train,y_test=train_test_split(x,y,train_size=0.7,random_state=42)

In $26$ :

def plr(degree=1,penalty='none',C=1.0):

pipe=make_pipeline(PolynomialFeatures(degree=degree,include_bias=False),

StandardScaler(),

LogisticRegression(penalty=penalty,tol=1e-4,C=C,max_iter=int(1e4)))

return pipe

#UI多迭代10的6次方次，tol是优化算法的收敛容忍度，c是正则化项参数

In $27$ :

pl1=plr()

In $28$ :

pl1.fit(x_train,y_train)

In $29$ :

pl1.score(x_train,y_train),pl1.score(x_test,y_test)

In $37$ :

def plot_decision_boundary(x,y,model):

x1,x2=np.meshgrid(

np.linspace(x $:,0$ .min()-1,x $:,0$ .max()+1,1000).reshape(-1,1),

np.linspace(x $:,1$ .min()-1,x $:,1$ .max()+1,1000).reshape(-1,1))

x_temp=np.concatenate( $x1.reshape(-1,1),x2.reshape(-1,1)$ ,1)

yhat_temp=model.predict(x_temp)

yhat=yhat_temp.reshape(x1.shape)

from matplotlib.colors import ListedColormap

custom_cmap=ListedColormap( $'#EF9A9A','#90CAF9'$ )

plt.contourf(x1,x2,yhat,cmap=custom_cmap)

plt.scatter(x $(y==0).flatten(),0$ ,x $(y==0).flatten(),1$ ,color='red')

plt.scatter(x $(y==1).flatten(),0$ ,x $(y==1).flatten(),1$ ,color='red')

In $38$ :

plot_decision_boundary(x,y,pl1)

In $39$ :

#再看下2次特征进行建模：

pr2=plr(degree=2)

In $40$ :

pr2.fit(x_train,y_train)

In $41$ :

pr2.score(x_train,y_train),pr2.score(x_test,y_test)

#(0.7914285714285715, 0.7866666666666666)，分数提升10%

In $43$ :

plot_decision_boundary(x,y,pr2)

In $45$ :

#如何查看参数情况

pr2.named_steps $'logisticregression'$ .coef_

#array( $\[-0.81012988, 0.04384694, -0.48583038, 0.02977868, -1.12352417$ ])

In $46$ :

#过拟合倾向实验

pr3=plr(degree=10)

pr3.fit(x_train,y_train)

pr3.score(x_train,y_train),pr3.score(x_test,y_test)

#(0.8314285714285714, 0.78)

In $47$ :

plot_decision_boundary(x,y,pr3)

In $48$ :

#尝试不同参数下准确率评分

score_l=\[\]

for degree in range(1,21):

pr_temp=plr(degree=degree)

pr_temp.fit(x_train,y_train)

score_temp= $pr_temp.score(x_train,y_train),pr_temp.score(x_test,y_test)$

score_l.append(score_temp)

In $49$ :

np.array(score_l)

In $54$ :

#画图看准确率变化

plt.plot(list(range(1,21)),np.array(score_l) $:,0$ ,label='train_acc')

plt.plot(list(range(1,21)),np.array(score_l) $:,1$ ,label='test_acc')

plt.legend(loc=4)#指定图例位置

In $55$ :

#手动调参，尝试LL1正则化

pl1=plr(degree=10,penalty='l1',C=1.0)

In $57$ :

pl1.set_params(logisticregression__solver='saga')

pl1.fit(x_train,y_train)#直接fit会报错，要改变求解器为saga

In $58$ :

pl1.score(x_train,y_train),pl1.score(x_test,y_test)

In $62$ :

#尝试枚举搜索参数有degree、C、正则化项

score_l1=\[\]

for degree in range(1,21):

pr_temp=plr(degree=degree,penalty='l1')

pr_temp.set_params(logisticregression__solver='saga')

pr_temp.fit(x_train,y_train)

score_temp= $pr_temp.score(x_train,y_train),pr_temp.score(x_test,y_test)$

score_l1.append(score_temp)

plt.plot(list(range(1,21)),np.array(score_l1) $:,0$ ,label='train_acc')

plt.plot(list(range(1,21)),np.array(score_l1) $:,1$ ,label='test_acc')

plt.legend(loc=4)#指定图例位置

In $63$ :

score_l1#打印发现degree=3是最优解，以此为degree进行后面的搜索

In $64$ :

score_l2=\[\]

for degree in range(1,21):

pr_temp=plr(degree=degree,penalty='l2')

pr_temp.set_params(logisticregression__solver='saga')

pr_temp.fit(x_train,y_train)

score_temp= $pr_temp.score(x_train,y_train),pr_temp.score(x_test,y_test)$

score_l2.append(score_temp)

plt.plot(list(range(1,21)),np.array(score_l2) $:,0$ ,label='train_acc')

plt.plot(list(range(1,21)),np.array(score_l2) $:,1$ ,label='test_acc')

plt.legend(loc=4)#指定图例位置

In $66$ :

score_l2#打印发现degree=15是最优解，以此为degree进行后面的搜索

In $72$ :

#尝试C的取值

score_l1_3=\[\]

for c in np.arange(0.5,2,0.1):

pr_temp=plr(degree=3,penalty='l1',C=c)

pr_temp.set_params(logisticregression__solver='saga')

pr_temp.fit(x_train,y_train)

score_temp= $pr_temp.score(x_train,y_train),pr_temp.score(x_test,y_test)$

score_l1_3.append(score_temp)

plt.plot(list(np.arange(0.5,2,0.1)),np.array(score_l1_3) $:,0$ ,label='train_acc')

plt.plot(list(np.arange(0.5,2,0.1)),np.array(score_l1_3) $:,1$ ,label='test_acc')

plt.legend(loc=4)#指定图例位置

In $73$ :

score_l1_3#因此准确率最高为0.8左右

机器学习实战笔记30-31：逻辑回归及对应调参实验代码

coding: utf-8

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