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逻辑回归三分类器#

下图展示了逻辑回归分类器在 iris 数据集的前两个维度（花萼长度和宽度）上的决策边界。数据点根据其标签进行着色。

# 代码来源：Gaël Varoquaux
# 由Jaques Grobler修改用于文档
# SPDX许可证标识符：BSD-3-Clause

import matplotlib.pyplot as plt

from sklearn import datasets
from sklearn.inspection import DecisionBoundaryDisplay
from sklearn.linear_model import LogisticRegression

# 导入一些数据来玩玩
iris = datasets.load_iris()
X = iris.data[:, :2]  # we only take the first two features.
Y = iris.target

# 创建一个逻辑回归分类器实例并拟合数据。
logreg = LogisticRegression(C=1e5)
logreg.fit(X, Y)

_, ax = plt.subplots(figsize=(4, 3))
DecisionBoundaryDisplay.from_estimator(
    logreg,
    X,
    cmap=plt.cm.Paired,
    ax=ax,
    response_method="predict",
    plot_method="pcolormesh",
    shading="auto",
    xlabel="Sepal length",
    ylabel="Sepal width",
    eps=0.5,
)

# 还要绘制训练点
plt.scatter(X[:, 0], X[:, 1], c=Y, edgecolors="k", cmap=plt.cm.Paired)


plt.xticks(())
plt.yticks(())

plt.show()