《python机器学习基础教程》代码实现线性模型--分类

Logistic回归

from sklearn.linear_model import LogisticRegression
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
cancer = load_breast_cancer()
X_train,X_test,y_train,y_test=train_test_split(cancer.data,cancer.target,stratify=cancer.target,random_state=42)
logreg=LogisticRegression().fit(X_train,y_train)
print("Training set score:{:.3f}".format(logreg.score(X_train,y_train)))
print("Test set score:{:.3f}".format(logreg.score(X_test,y_test)))
logreg100=LogisticRegression(C=100).fit(X_train,y_train)
print("Training set score:{:.3f}".format(logreg100.score(X_train,y_train)))
print("Test set score:{:.3f}".format(logreg100.score(X_test,y_test)))
logreg0001=LogisticRegression(C=0.01).fit(X_train,y_train)
print("Training set score:{:.3f}".format(logreg0001.score(X_train,y_train)))
print("Test set score:{:.3f}".format(logreg0001.score(X_test,y_test)))
plt.plot(logreg.coef_.T,'o',label="C=1")
plt.plot(logreg100.coef_.T,'^',label="C=100")
plt.plot(logreg0001.coef_.T,'v',label="C=0.001")
plt.xticks(range(cancer.data.shape[1]),cancer.feature_names, rotation=90)
plt.hlines(0,0,cancer.data.shape[1])
plt.ylim(-5,5)
plt.xlabel("Coefficient index")
plt.ylabel("Coefficient magnitude")
plt.legend()
plt.show()

线性支持向量机SVM

from sklearn.datasets import make_blobs
import mglearn
import matplotlib.pyplot as plt
from sklearn.svm import LinearSVC
import numpy as np
X,y = make_blobs(random_state=42)
mglearn.discrete_scatter(X[:,0],X[:,1],y)
linearsvm = LinearSVC().fit(X,y)
mglearn.plots.plot_2d_classification(linearsvm,X,fill=True,alpha=0.7)
line = np.linspace(-15,15)
for coef,intercept,color in zip(linearsvm.coef_,linearsvm.intercept_,['b','r','g']):
    plt.plot(line,-(line*coef[0]+intercept)/coef[1],c=color)
plt.ylim(-10,15)
plt.xlim(-10,8)
plt.xlabel("Feature 0")
plt.ylabel("Feature 1")
plt.legend(["Class 0","Class 1","Class 2","Line class 0","Line class 1","Line class 2"],loc=(1.01,0.3))
plt.show()