通过交叉验证，Sklearn中的梯度提升的调整参数

Question

假设x_train处于(751、411(的形状，而y__train的形状为(751L，(。我想使用网格搜索使用交叉验证来找到GBR的最佳参数。我使用了以下代码，但无法成功。

 from sklearn.grid_search import GridSearchCV 
 param_grid={'n_estimators':[100,500], 
            'learning_rate': [0.1,0.05,0.02],
            'max_depth':[4], 
            'min_samples_leaf':[3], 
            'max_features':[1.0] } 
n_jobs=4
def GradientBooster(param_grid, n_jobs):
    estimator = GradientBoostingRegressor() 
    classifier = GridSearchCV(estimator=estimator, cv=5, param_grid=param_grid, 
    n_jobs=n_jobs)
    classifier.fit(X_train, Y_train)
    print classifier.best_estimator_ 
return cv, classifier.best_estimator_ 
cv,best_est=GradientBooster(param_grid, n_jobs)

它给我以下错误：

     51         from pandas.core.config import get_option
     52 
     53         encoding = get_option("display.encoding")
---> 54         return self.__unicode__().encode(encoding, 'replace')
        self.__unicode__.encode = undefined
        encoding = 'cp0'
     55 
     56     def __repr__(self):
     57         """
     58         Return a string representation for a particular object.
LookupError: unknown encoding: cp0

然后，我想使用这些参数使用预测函数来预测X_test。

我在以下代码上也有同样的问题：

param_grid = {
    'bootstrap': [True],
    'max_depth': [80, 90, 100, 110],
    'max_features': [2, 3],
    'min_samples_leaf': [3, 4, 5],
    'min_samples_split': [8, 10, 12],
    'n_estimators': [100, 200, 300, 1000]
}
rf = RandomForestRegressor()
grid_search = GridSearchCV(estimator = rf, param_grid = param_grid, 
                          cv = 3, n_jobs = -1, verbose = 2)
grid_search.fit(X_train, Y_train)

Answer 1

这是测试数据集的工作示例

from sklearn.datasets import load_boston
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import GridSearchCV
X,y = load_boston(return_X_y=True)
param_grid = {
    'bootstrap': [True],
    'max_depth': [80, 90, 100, 110],
    'max_features': [2, 3],
    'min_samples_leaf': [3, 4, 5],
    'min_samples_split': [8, 10, 12],
    'n_estimators': [100, 200, 300, 1000]
}
rf = RandomForestRegressor()
grid_search = GridSearchCV(estimator = rf, param_grid = param_grid, 
                          cv = 3, n_jobs = -1, verbose = 2)
grid_search.fit(X, y)

您的数据很可能存在问题。