Python实现SVM支持向量机的示例代码

from sklearn import svm
from sklearn.datasets import make_classification

# generate some example data
X, y = make_classification(n_features=4, random_state=0)

# fit an SVM model to the data
clf = svm.SVC(kernel='linear')
clf.fit(X, y)

# predict new data
print(clf.predict([[0, 0, 0, 0]]))

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score

# 加载数据集
iris = datasets.load_iris()
X = iris["data"]
y = iris["target"]

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

# 创建 SVM 模型
model = SVC(kernel='linear', C=1, random_state=0)

# 训练模型
model.fit(X_train, y_train)

# 在测试集上进行预测
y_pred = model.predict(X_test)

# 计算预测精度
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score

# 加载数据集
iris = datasets.load_iris()
X = iris["data"]
y = iris["target"]

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

# 创建 SVM 模型，使用高斯核函数
model = SVC(kernel='rbf', C=1, random_state=0)

# 训练模型
model.fit(X_train, y_train)

# 在测试集上进行预测
y_pred = model.predict(X_test)

# 计算预测精度
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)