SVM

支持向量机

一、算法原理

二、scikit-learn SVM

from sklearn.svm import LinearSVC
svm_clf = LinearSVC(C=1)
svm_clf.fit(X, y)

三、鸢尾花示例

import numpy as np
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import LinearSVC

# 导入数据集
from sklearn.datasets import fetch_openml
iris = fetch_openml(name='iris')

# 切分数据集
X = iris['data'][:, 2:]
y = (iris['target'] == 'Iris-versicolor').astype(np.float64)

svm_clf = Pipeline([
    ('scalar', StandardScaler()),
    ('liner_svm', LinearSVC(C=1))
])

# 训练模型
svm_clf.fit(X, y)

# 预测
svm_clf.predict([[5.5, 1.7]])

运行结果