ML_Logistic_Regression

机器学习100天系列学习笔记机器学习100天(中文翻译版)机器学习100天(英文原版）
代码阅读：

第一步：导包

#Step 1: Importing the Libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

第二步：导入数据

#Step 2: Importing the dataset
dataset = pd.read_csv('D:/daily/机器学习100天/100-Days-Of-ML-Code-中文版本/100-Days-Of-ML-Code-master/datasets/Social_Network_Ads.csv')
X = dataset.iloc[:, [2, 3]].values
y = dataset.iloc[:, 4].values

第三步：划分训练集、测试集

#Step 3: Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)

第四步：特征缩放

#Step 4: Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

经过特征缩放后的X_train:

[[ 0.58164944 -0.88670699][-0.60673761  1.46173768][-0.01254409 -0.5677824 ][-0.60673761  1.89663484][ 1.37390747 -1.40858358][ 1.47293972  0.99784738][ 0.08648817 -0.79972756][-0.01254409 -0.24885782][-0.21060859 -0.5677824 ]...]

对于进行特征缩放这一步，个人认为是非常重要的，它可以加快计算速度，在深度学习中间尤为重要（梯度爆炸问题）。

第五步：Logistic Regression

#Step 5: Fitting Logistic Regression to the Training set
from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression()
classifier.fit(X_train, y_train)

第六步：预测

#Step 6: Predicting the Test set results
y_pred = classifier.predict(X_test)

第七步：混淆矩阵

#Step 7: Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
cm = confusion_matrix(y_test, y_pred)
print(cm)  # print confusion_matrix
print(classification_report(y_test, y_pred))   # print classification report

混淆：简单理解为一个class被预测成另一个class。
给一个参考链接混淆矩阵

第八步：可视化

#Step 8: Visualization
from matplotlib.colors import ListedColormap
X_set,y_set = X_train,y_train
X1,X2 = np. meshgrid(np. arange(start=X_set[:,0].min()-1, stop=X_set[:,0].max()+1, step=0.01),np. arange(start=X_set[:,1].min()-1, stop=X_set[:,1].max()+1, step=0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(),X2.ravel()]).T).reshape(X1.shape),alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(),X1.max())
plt.ylim(X2.min(),X2.max())for i,j in enumerate(np.unique(y_set)):plt.scatter(X_set[y_set==j,0],X_set[y_set==j,1],c = ListedColormap(('red', 'green'))(i), label=j)
plt. title(' LOGISTIC(Training set)')
plt. xlabel(' Age')
plt. ylabel(' Estimated Salary')
plt. legend()
plt. show()X_set,y_set=X_test,y_test
X1,X2=np. meshgrid(np. arange(start=X_set[:,0].min()-1, stop=X_set[:, 0].max()+1, step=0.01),np. arange(start=X_set[:,1].min()-1, stop=X_set[:,1].max()+1, step=0.01))plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(),X2.ravel()]).T).reshape(X1.shape),alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(),X1.max())
plt.ylim(X2.min(),X2.max())
for i,j in enumerate(np. unique(y_set)):plt.scatter(X_set[y_set==j,0],X_set[y_set==j,1],c = ListedColormap(('red', 'green'))(i), label=j)plt. title(' LOGISTIC(Test set)')
plt. xlabel(' Age')
plt. ylabel(' Estimated Salary')
plt. legend()
plt. show()

可视化这一部分代码可能比较难理解，函数meshgrid是生成一个二维矩阵，大小为X*Y数据个数，这里为(592, 616)；函数ravel将矩阵X1（二维）矩阵展开为一维矩阵（364672，）；函数reshape将（364672，）转为跟矩阵X1大小一致的二维矩阵；
参数alpha为透明度；函数unique返回参数数组y_set中所有不同的值，并按照从小到大排序，这里返回（0，1）；函数enumerate() 用于将一个可遍历的数据对象enumerate；这里循环i，j取0或1，
标签y_setj成立，True=1，标签y_setj不成立，Flase=0，所以0为红色点，1为绿色点。
做出的图为：

全部代码：

#Day 4: Simple Linear Regression 2022/4/7
#Step 1: Importing the Libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd#Step 2: Importing the dataset
dataset = pd.read_csv('D:/daily/机器学习100天/100-Days-Of-ML-Code-中文版本/100-Days-Of-ML-Code-master/datasets/Social_Network_Ads.csv')
X = dataset.iloc[:, [2, 3]].values
y = dataset.iloc[:, 4].values#Step 3: Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)#Step 4: Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
#print(X_train)#Step 5: Fitting Logistic Regression to the Training set
from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression()
classifier.fit(X_train, y_train)#Step 6: Predicting the Test set results
y_pred = classifier.predict(X_test)#Step 7: Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
cm = confusion_matrix(y_test, y_pred)
print(cm)  # print confusion_matrix
print(classification_report(y_test, y_pred))   # print classification report#Step 8: Visualization
from matplotlib.colors import ListedColormap
X_set,y_set = X_train,y_train
X1,X2 = np. meshgrid(np. arange(start=X_set[:,0].min()-1, stop=X_set[:,0].max()+1, step=0.01),np. arange(start=X_set[:,1].min()-1, stop=X_set[:,1].max()+1, step=0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(),X2.ravel()]).T).reshape(X1.shape),alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(),X1.max())
plt.ylim(X2.min(),X2.max())for i,j in enumerate(np.unique(y_set)):plt.scatter(X_set[y_set==j,0],X_set[y_set==j,1],c = ListedColormap(('red', 'green'))(i), label=j)
plt. title(' LOGISTIC(Training set)')
plt. xlabel(' Age')
plt. ylabel(' Estimated Salary')
plt. legend()
plt. show()X_set,y_set=X_test,y_test
X1,X2=np. meshgrid(np. arange(start=X_set[:,0].min()-1, stop=X_set[:, 0].max()+1, step=0.01),np. arange(start=X_set[:,1].min()-1, stop=X_set[:,1].max()+1, step=0.01))plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(),X2.ravel()]).T).reshape(X1.shape),alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(),X1.max())
plt.ylim(X2.min(),X2.max())
for i,j in enumerate(np. unique(y_set)):plt.scatter(X_set[y_set==j,0],X_set[y_set==j,1],c = ListedColormap(('red', 'green'))(i), label=j)plt. title(' LOGISTIC(Test set)')
plt. xlabel(' Age')
plt. ylabel(' Estimated Salary')
plt. legend()
plt. show()