与gridsearchCV相比，获取的准确度较低。

与GridSearchCV相比，获取的准确度较低可能是由于参数空间不充分或者搜索范围不合适导致的。下面是一些解决方法的示例代码：

1. 增加参数空间的范围：

from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestClassifier

param_grid = {'n_estimators': [50, 100, 150, 200],
              'max_depth': [None, 5, 10, 15]}

grid_search = GridSearchCV(RandomForestClassifier(), param_grid, cv=5)
grid_search.fit(X_train, y_train)

print("最佳参数：", grid_search.best_params_)
print("准确度：", grid_search.best_score_)

2. 增加搜索的交叉验证次数：

from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestClassifier

param_grid = {'n_estimators': [50, 100, 150, 200],
              'max_depth': [None, 5, 10, 15]}

grid_search = GridSearchCV(RandomForestClassifier(), param_grid, cv=10)
grid_search.fit(X_train, y_train)

print("最佳参数：", grid_search.best_params_)
print("准确度：", grid_search.best_score_)

3. 尝试使用更复杂的模型：

from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import GradientBoostingClassifier

param_grid = {'n_estimators': [50, 100, 150, 200],
              'max_depth': [None, 5, 10, 15]}

grid_search = GridSearchCV(GradientBoostingClassifier(), param_grid, cv=5)
grid_search.fit(X_train, y_train)

print("最佳参数：", grid_search.best_params_)
print("准确度：", grid_search.best_score_)

4. 使用其他评估指标进行搜索：

from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import f1_score

param_grid = {'n_estimators': [50, 100, 150, 200],
              'max_depth': [None, 5, 10, 15]}

grid_search = GridSearchCV(RandomForestClassifier(), param_grid, cv=5, scoring='f1_macro')
grid_search.fit(X_train, y_train)

print("最佳参数：", grid_search.best_params_)
print("F1得分：", grid_search.best_score_)

通过调整参数空间、增加交叉验证次数、尝试更复杂的模型或者使用其他评估指标，可以提高GridSearchCV的准确度。