线性分类模型Linear models for classification

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于 2021-05-05 11:44:26 发布

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分类专栏：机器学习文章标签：机器学习 python 分类算法

本文链接：https://blog.youkuaiyun.com/liaozhaocong/article/details/116423002

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1、二分类

from sklearn.linear_model import LogisticRegression
from sklearn.svm import LinearSVC

X, y = mglearn.datasets.make_forge()#导入数据

fig, axes = plt.subplots(1, 2, figsize=(10, 3))#1行2列

for model, ax in zip([LinearSVC(), LogisticRegression()], axes): #模型取=LinearSVC(), LogisticRegression()
    clf = model.fit(X, y)
    mglearn.plots.plot_2d_separator(clf, X, fill=False, eps=0.5,
                                    ax=ax, alpha=.7)
    mglearn.discrete_scatter(X[:, 0], X[:, 1], y, ax=ax)
    ax.set_title(clf.__class__.__name__)
    ax.set_xlabel("Feature 0")
    ax.set_ylabel("Feature 1")
axes[0].legend()

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mglearn.plots.plot_linear_svc_regularization()

使用正则化
在这里插入图片描述

from sklearn.datasets import load_breast_cancer
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