svc =SVC(C=1000, gamma='auto') svc.fit(X_train_scaled,y_train) print("训练集精度:[:.3f]".format(svc.score(X_train_scaled, y_train))) print("svc.C",svc.C) print("svc.gamma=",svc.gamma) print('='*20) for c_value in[1,10,100,1000]: svc=SVC(C=c_value,gamma='auto') svc.fit(X_train_scaled,y_train) print('C='c_value) print("Training set score:[:.3f]".format(svc.score(X_train_scaled, y_train))) print("Test set score:[:.3f}".format(svc.score(X_train_scaled, y_test))) print('-'*20) pass修改代码
时间: 2024-02-22 15:57:58 浏览: 26
修改后的代码如下:
```
svc = SVC(C=1000, gamma='auto')
svc.fit(X_train_scaled, y_train)
print("训练集精度:{:.3f}".format(svc.score(X_train_scaled, y_train)))
print("svc.C =", svc.C)
print("svc.gamma =", svc.gamma)
print('='*20)
for c_value in [1, 10, 100, 1000]:
svc = SVC(C=c_value, gamma='auto')
svc.fit(X_train_scaled, y_train)
print('C=', c_value)
print("Training set score: {:.3f}".format(svc.score(X_train_scaled, y_train)))
print("Test set score: {:.3f}".format(svc.score(X_test_scaled, y_test)))
print('-'*20)
```
我将代码中的 print 语句中的括号和引号进行了修正,使其符合 Python 语法规范。另外,我还在第二个 for 循环中的测试集精度计算中,将 X_train_scaled 改为 X_test_scaled,使其输出正确的测试集精度。
相关问题
min_on_training = X_train.min(axis=0) range_on_training = (X_train - min_on_training).max(axis=0) X_train_scaled = (X_train - min_on_training) / range_on_training print(("Minimum for each feature\n", X_train_scaled.min(axis=0))) print(("Maximum for each feature\n", X_train_scaled.max(axis=0))) svc = SVC(gamma='auto') SVC.fit(X_train_scaled, y_train) print("训练集精度:[:.3f]".format(svc.score(X_train_scaled, y_train))) print("训练集精度:[:.3f]".format(svc.score(X_train_scaled, y_test)))修改代码
代码中有两处小错误,应该将 `SVC.fit(X_train_scaled, y_train)` 改为 `svc.fit(X_train_scaled, y_train)`,并且在打印训练集精度时,应该将 `[:.3f]` 改为 `{:.3f}`。修改后的代码如下:
```
min_on_training = X_train.min(axis=0)
range_on_training = (X_train - min_on_training).max(axis=0)
X_train_scaled = (X_train - min_on_training) / range_on_training
print("Minimum for each feature:\n", X_train_scaled.min(axis=0))
print("Maximum for each feature:\n", X_train_scaled.max(axis=0))
svc = SVC(gamma='auto')
svc.fit(X_train_scaled, y_train)
print("训练集精度:{:.3f}".format(svc.score(X_train_scaled, y_train)))
print("测试集精度:{:.3f}".format(svc.score(X_test, y_test)))
```
注意,在打印精度时,第二个应该是测试集的精度,所以将 `X_train_scaled` 改成了 `X_test`。
优化model=SVC(kernel='rbf',random_state=123) model.fit(x_train_s,y_train)
这段代码使用了SVM中的径向基函数(RBF)作为核函数进行分类模型的训练。以下是对代码的优化:
1. 使用 `C` 和 `gamma` 参数进行网格搜索,以找到最佳的模型超参数。
2. 使用 `Pipeline` 对数据进行预处理和模型训练,将数据预处理和模型训练合并在一起,提高代码的可读性和可维护性。
3. 使用 `GridSearchCV` 进行交叉验证和超参数选择,避免手动调节超参数和可能的过拟合。
```python
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
# 定义数据预处理和模型训练流程
pipeline = Pipeline([
('scaler', StandardScaler()), # 数据标准化
('svm', SVC(kernel='rbf', random_state=123)) # SVM模型
])
# 定义超参数范围
param_grid = {
'svm__C': [0.1, 1, 10],
'svm__gamma': [0.01, 0.1, 1]
}
# 使用交叉验证进行超参数选择
grid_search = GridSearchCV(pipeline, param_grid=param_grid, cv=5)
grid_search.fit(x_train_s, y_train)
# 输出最佳模型超参数和交叉验证得分
print('Best hyperparameters:', grid_search.best_params_)
print('Cross-validation score:', grid_search.best_score_)
# 训练最佳模型
best_model = grid_search.best_estimator_
best_model.fit(x_train_s, y_train)
# 输出在测试集上的预测准确率
test_score = best_model.score(x_test_s, y_test)
print('Test score:', test_score)
```
这个优化后的代码使用了交叉验证进行超参数选择,避免了手动调节超参数和过拟合的问题,同时使用了 `Pipeline` 对数据预处理和模型训练流程进行封装,提高了代码的可读性和可维护性。