sklearn使用高斯核SVM显示支持向量

最新推荐文章于 2024-10-21 21:27:24 发布

原创最新推荐文章于 2024-10-21 21:27:24 发布 · 719 阅读

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本文通过使用Python的scikit-learn库中的SVM（支持向量机）分类器，对人工创建的数据集进行分类。展示了如何训练模型，绘制决策边界，并突出显示支持向量。同时，解释了支持向量的类别标签由双系数的符号决定。

部署运行你感兴趣的模型镜像

import graphviz
import mglearn
from mpl_toolkits.mplot3d import Axes3D
from sklearn.datasets import load_breast_cancer, make_blobs
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier, export_graphviz
from IPython.display import display
import matplotlib.pyplot as plt
import numpy as np
import matplotlib as mt
import pandas as pd

X, y = mglearn.tools.make_handcrafted_dataset()
svm = SVC(kernel='rbf', C=100, gamma=0.1).fit(X, y)
mglearn.plots.plot_2d_separator(svm, X, eps=.5)
mglearn.discrete_scatter(X[:, 0], X[:, 1], y)
# plot support vectors
sv = svm.support_vectors_
print(sv)
# class labels of support vectors are given by the sign of the dual coefficients
sv_labels = svm.dual_coef_.ravel() > 0
mglearn.discrete_scatter(sv[:, 0], sv[:, 1], sv_labels, s=15, markeredgewidth=3)
plt.xlabel("Feature 0")
plt.ylabel("Feature 1")
plt.show()