scikit-learn中的SVM

最新推荐文章于 2025-06-09 10:24:51 发布

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最新推荐文章于 2025-06-09 10:24:51 发布

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分类专栏： sklearn

本文链接：https://blog.youkuaiyun.com/qq_36264495/article/details/88295451

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本文通过使用scikit-learn库中的SVM（支持向量机）算法，对鸢尾花数据集进行分类任务。文章详细展示了数据预处理、特征标准化、模型训练及决策边界绘制的过程，对比了不同C值下SVM模型的表现。

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scikit-learn中的SVM

import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
iris = datasets.load_iris()

X = iris.data
y = iris.target
X = X[y<2,:2]
y = y[y<2]

plt.scatter(X[y==0,0], X[y==0,1], color='red')
plt.scatter(X[y==1,0], X[y==1,1], color='blue')
plt.show()

在这里插入图片描述

涉及到距离，量纲就有可能不一样，所以要标准化。

from sklearn.preprocessing import StandardScaler
standardScaler = StandardScaler()
standardScaler.fit(X)
X_standard = standardScaler.transform(X)

C值越大，容错越小

from sklearn.svm import LinearSVC
svc = LinearSVC(C=1e9)
svc.fit(X_standard, y)

LinearSVC(C=1000000000.0, class_weight=None, dual=True, fit_intercept=True, intercept_scaling=1, loss='squared_hinge', max_iter=1000, multi_class='ovr', penalty='l2', random_state=None, tol=0.0001, verbose=0)

决策边界的函数，传入的是模型和边界

def plot_decision_boundary(model, axis):
    
    x0, x1 = np.meshgrid(
        np.linspace(axis[0], axis[1], int((axis[1]-axis[0])*100)).reshape(-1, 1),
        np.linspace(axis[2], axis[3], int((axis[3]-axis[2])*100)).reshape(-1, 1),
    )
    X_new = np.c_[x0.ravel(), x1.ravel()]
    y_predict = model.predict(X_new)
    zz = y_predict.reshape(x0.shape)
    from matplotlib.colors import ListedColormap
    custom_cmap = ListedColormap(['#EF9A9A','#FFF59D','#90CAF9'])
    plt.contourf(x0, x1, zz, linewidth=5, cmap=custom_cmap)

plot_decision_boundary(svc, axis=[-3, 3, -3, 3])
plt.scatter(X_standard[y==0,0], X_standard[y==0,1])
plt.scatter(X_standard[y==1,0], X_standard[y==1,1])
plt.show()

在这里插入图片描述

svc2 = LinearSVC(C=0.01)
svc2.fit(X_standard, y)

LinearSVC(C=0.01, class_weight=None, dual=True, fit_intercept=True, intercept_scaling=1, loss='squared_hinge', max_iter=1000, multi_class='ovr', penalty='l2', random_state=None, tol=0.0001, verbose=0)

plot_decision_boundary(svc2, axis=[-3, 3, -3, 3])
plt.scatter(X_standard[y==0,0], X_standard[y==0,1])
plt.scatter(X_standard[y==1,0], X_standard[y==1,1])
plt.show()

在这里插入图片描述

svc.coef_

array([[ 4.03243305, -2.49295041]])

svc.intercept_

array([ 0.9536471])

SVM决策边界的函数，包括上下两根线

def plot_svc_decision_boundary(model, axis):
    
    x0, x1 = np.meshgrid(
        np.linspace(axis[0], axis[1], int((axis[1]-axis[0])*100)).reshape(-1, 1),
        np.linspace(axis[2], axis[3], int((axis[3]-axis[2])*100)).reshape(-1, 1),
    )
    X_new = np.c_[x0.ravel(), x1.ravel()]

    y_predict = model.predict(X_new)
    zz = y_predict.reshape(x0.shape)

    from matplotlib.colors import ListedColormap
    custom_cmap = ListedColormap(['#EF9A9A','#FFF59D','#90CAF9'])
    
    plt.contourf(x0, x1, zz, linewidth=5, cmap=custom_cmap)
    
    w = model.coef_[0]
    b = model.intercept_[0]
    
    # w0*x0 + w1*x1 + b = 0
    # => x1 = -w0/w1 * x0 - b/w1
    plot_x = np.linspace(axis[0], axis[1], 200)
    up_y = -w[0]/w[1] * plot_x - b/w[1] + 1/w[1]
    down_y = -w[0]/w[1] * plot_x - b/w[1] - 1/w[1]
    
    up_index = (up_y >= axis[2]) & (up_y <= axis[3])
    down_index = (down_y >= axis[2]) & (down_y <= axis[3])
    plt.plot(plot_x[up_index], up_y[up_index], color='black')
    plt.plot(plot_x[down_index], down_y[down_index], color='black')

plot_svc_decision_boundary(svc, axis=[-3, 3, -3, 3])
plt.scatter(X_standard[y==0,0], X_standard[y==0,1])
plt.scatter(X_standard[y==1,0], X_standard[y==1,1])
plt.show()

在这里插入图片描述

plot_svc_decision_boundary(svc2, axis=[-3, 3, -3, 3])
plt.scatter(X_standard[y==0,0], X_standard[y==0,1])
plt.scatter(X_standard[y==1,0], X_standard[y==1,1])
plt.show()

在这里插入图片描述