在虹膜数据集中绘制不同的支持者分类器#

在虹膜数据集的2D投影上比较不同线性支持者。我们只考虑该数据集的前2个特征：

花瓣长度
花瓣宽度

这个例子展示了如何绘制具有不同内核的四个SVM分类器的决策曲面。

线性模型 LinearSVC() 和 SVC(kernel='linear') 会产生稍微不同的决策边界。这可能是以下差异的结果：

LinearSVC 最大限度地减少铰链损失的平方， SVC 最大限度地减少常规铰链损失。
LinearSVC 使用One-vs-All（也称为One-vs-Rest）多类简化， SVC 使用一对一多类简化。

两种线性模型都具有线性决策边界（交叉超平面），而非线性核模型（多项或高斯基函数）具有更灵活的非线性决策边界，其形状取决于核的类型及其参数。

备注

虽然绘制玩具2D数据集分类器的决策函数可以帮助直观地理解它们各自的表达能力，但请注意，这些直觉并不总是概括为更现实的多维问题。

SVC with linear kernel, LinearSVC (linear kernel), SVC with RBF kernel, SVC with polynomial (degree 3) kernel

# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause

import matplotlib.pyplot as plt

from sklearn import datasets, svm
from sklearn.inspection import DecisionBoundaryDisplay

# import some data to play with
iris = datasets.load_iris()
# Take the first two features. We could avoid this by using a two-dim dataset
X = iris.data[:, :2]
y = iris.target

# we create an instance of SVM and fit out data. We do not scale our
# data since we want to plot the support vectors
C = 1.0  # SVM regularization parameter
models = (
    svm.SVC(kernel="linear", C=C),
    svm.LinearSVC(C=C, max_iter=10000),
    svm.SVC(kernel="rbf", gamma=0.7, C=C),
    svm.SVC(kernel="poly", degree=3, gamma="auto", C=C),
)
models = (clf.fit(X, y) for clf in models)

# title for the plots
titles = (
    "SVC with linear kernel",
    "LinearSVC (linear kernel)",
    "SVC with RBF kernel",
    "SVC with polynomial (degree 3) kernel",
)

# Set-up 2x2 grid for plotting.
fig, sub = plt.subplots(2, 2)
plt.subplots_adjust(wspace=0.4, hspace=0.4)

X0, X1 = X[:, 0], X[:, 1]

for clf, title, ax in zip(models, titles, sub.flatten()):
    disp = DecisionBoundaryDisplay.from_estimator(
        clf,
        X,
        response_method="predict",
        cmap=plt.cm.coolwarm,
        alpha=0.8,
        ax=ax,
        xlabel=iris.feature_names[0],
        ylabel=iris.feature_names[1],
    )
    ax.scatter(X0, X1, c=y, cmap=plt.cm.coolwarm, s=20, edgecolors="k")
    ax.set_xticks(())
    ax.set_yticks(())
    ax.set_title(title)

plt.show()

Total running time of the script: （0分0.136秒）