生成对鸢尾花数据集非线性分类，含有最优超参数并且进行可视化的python代码

好的，以下是生成对鸢尾花数据集非线性分类、含有最优超参数并进行可视化的Python代码:

# 导入所需库
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV

# 加载鸢尾花数据集，并进行划分
iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, random_state=0)

# 构建SVM模型
svm = Pipeline([
    ("scaler", StandardScaler()),   # 数据标准化
    ("svc", SVC(kernel="rbf"))      # 核函数采用径向基函数
])

# 设定超参数网格
param_grid = {
    "svc__C": [0.1, 1, 10, 100],
    "svc__gamma": [0.1, 1, 10, 100]
}

# 使用GridSearchCV寻找最优超参数
grid = GridSearchCV(svm, param_grid=param_grid, cv=5)
grid.fit(X_train, y_train)

# 输出最优超参数并用来构建最终模型
print("Best cross-validation accuracy: {:.2f}".format(grid.best_score_))
print("Best parameters: ", grid.best_params_)
svm = grid.best_estimator_

# 画出决策边界
def plot_decision_boundary(model, ax=None):
    if ax is None:
        ax = plt.gca()
    x_min, x_max = ax.get_xlim()
    y_min, y_max = ax.get_ylim()
    
    xx, yy = np.meshgrid(np.linspace(x_min, x_max, 100), np.linspace(y_min, y_max, 100))
    Z = model.predict(np.c_[xx.ravel(), yy.ravel()])
    Z = Z.reshape(xx.shape)
    
    # 画出等高线
    ax.contourf(xx, yy, Z, alpha=0.3)
    
    # 设置x轴和y轴的范围及标签
    ax.set_xlim(x_min, x_max)
    ax.set_ylim(y_min, y_max)
    ax.set_xlabel("Feature 0")
    ax.set_ylabel("Feature 1")

# 将数据集降为二维
X_train = X_train[:, :2]

# 构建SVM模型，用最优超参数重新训练
svm = Pipeline([
    ("scaler", StandardScaler()),
    ("svc", SVC(kernel="rbf", C=grid.best_params_["svc__C"], gamma=grid.best_params_["svc__gamma"]))
])
svm.fit(X_train, y_train)

# 画出决策边界和训练数据点
plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=plt.cm.Set1, edgecolor='k')
plot_decision_boundary(svm)
plt.show()

希望以上代码可以对您有所帮助！