逻辑回归（Linear Regression）
第1步：数据预处理
导入库
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
导入数据集
<a href = "https://github.com/Avik-Jain/100-Days-Of-ML-Code/blob/master/datasets/Social_Network_Ads.csv">这里</a>获取数据集
dataset = pd.read_csv('../datasets/Social_Network_Ads.csv')
X = dataset.iloc[:, [2, 3]].values
Y = dataset.iloc[:,4].values
dataset.head()
User ID Gender Age EstimatedSalary Purchased
0 15624510 Male 19 19000 0
1 15810944 Male 35 20000 0
2 15668575 Female 26 43000 0
3 15603246 Female 27 57000 0
4 15804002 Male 19 76000 0
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)
特征缩放
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
c:\users\administrator\appdata\local\programs\python\python36\lib\site-packages\sklearn\utils\validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.
warnings.warn(msg, DataConversionWarning)
第二步：逻辑回归模型
该项工作的库将会是一个线性模型库，之所以被称为线性是因为逻辑回归是一个线性分类器，这意味着我们在二维空间中，我们两类用户（购买和不购买）将被一条直线分割。然后导入逻辑回归类。下一步我们将创建该类的对象，它将作为我们训练集的分类器。
将逻辑回归应用于训练集
from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression()
classifier.fit(X_train, Y_train)
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
verbose=0, warm_start=False)
第3步：预测
预测测试集结果
y_pred = classifier.predict(X_test)
第4步：评估预测
我们预测了测试集。 现在我们将评估逻辑回归模型是否正确的学习和理解。因此这个混淆矩阵将包含我们模型的正确和错误的预测。
生成混淆矩阵
from sklearn.metrics import confusion_matrix
confusion_matrix(Y_test, y_pred)
array([[65, 3],
[ 8, 24]], dtype=int64)
可视化
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()
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