from sklearn import svm import pandas as pd import numpy as np from sklearn.model_selection import train_test_split data = pd.read_csv( 'final_data1.csv') Y = data.y X = data.drop('y', axis=1) X_train, X_test, y_train, y_test = train_test_split(X_norm, Y, test_size=0.2, random_state=42) clf = svm.SVC() clf.fit(X_train, y_train) y_pred = clf.predict(X_test) y_pred= np.round(y_pred) 对上述代码进行GridSearchCV网格搜索调参的代码

下面是使用GridSearchCV进行网格搜索调参的示例代码：

from sklearn import svm
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split, GridSearchCV

data = pd.read_csv('final_data1.csv')
Y = data.y
X = data.drop('y', axis=1)
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=42)

# 定义参数网格
param_grid = {'C': [0.1, 1, 10], 'gamma': [0.1, 1, 10], 'kernel': ['linear', 'rbf']}

# 创建SVM分类器
clf = svm.SVC()

# 使用GridSearchCV进行网格搜索调参
grid_search = GridSearchCV(clf, param_grid, cv=5)
grid_search.fit(X_train, y_train)

# 输出最佳参数组合和对应的准确率
print("Best Parameters: ", grid_search.best_params_)
print("Best Accuracy: ", grid_search.best_score_)

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

在上述代码中，我们首先定义了一个参数网格`param_grid`，其中包含了需要调整的超参数的候选值。然后，创建了一个SVM分类器`clf`。接着，使用GridSearchCV进行网格搜索，传入分类器对象`clf`、参数网格`param_grid`和交叉验证的折数`cv`。调用`fit()`方法进行网格搜索调参。最后，输出最佳参数组合和对应的准确率。在测试集上进行预测时，使用调优后的模型进行预测。

请根据实际问题和数据集调整参数网格`param_grid`的范围，以及其他可能需要调整的参数。