Python 2.7 执行python scikit学习网格搜索方法时出现无效参数错误

我试图学习如何使用scikit learn中的GridSearchCV（）方法在决策树分类器中找到最佳超参数

问题是，如果我只指定一个参数的选项，这很好，如下所示：

print(__doc__)

# Code source: Gael Varoquaux
# Modified for documentation by Jaques Grobler
# License: BSD 3 clause

from sklearn import datasets
from sklearn.grid_search import GridSearchCV
from sklearn.tree import DecisionTreeClassifier

# define classifier
dt = DecisionTreeClassifier()

# import some data to play with
iris = datasets.load_iris()
X = iris.data[:, :2]  # we only take the first two features.
y = iris.target

# define parameter values that should be searched
min_samples_split_options = range(2, 4)

# create a parameter grid: map the parameter names to the values that should be saved
param_grid_dt = dict(min_samples_split= min_samples_split_options) # for DT

# instantiate the grid
grid = GridSearchCV(dt, param_grid_dt, cv=10, scoring='accuracy')

# fit the grid with param
grid.fit(X, y)

# view complete results
grid.grid_scores_

'''# examine results from first tuple
print grid.grid_scores_[0].parameters
print grid.grid_scores_[0].cv_validation_scores
print grid.grid_scores_[0].mean_validation_score'''

# examine the best model
print '*******Final results*********'
print grid.best_score_
print grid.best_params_
print grid.best_estimator_

结果:

None
*******Final results*********
0.68
{'min_samples_split': 3}
DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
            min_samples_split=3, min_weight_fraction_leaf=0.0,
            presort=False, random_state=None, splitter='best')

但是，当我在混合中添加其他参数的选项时，它会给我一个“无效参数”错误，如下所示：

print(__doc__)


# Code source: Gael Varoquaux
# Modified for documentation by Jaques Grobler
# License: BSD 3 clause

from sklearn import datasets
from sklearn.grid_search import GridSearchCV
from sklearn.tree import DecisionTreeClassifier

# define classifier
dt = DecisionTreeClassifier()

# import some data to play with
iris = datasets.load_iris()
X = iris.data[:, :2]  # we only take the first two features.
y = iris.target

# define parameter values that should be searched
max_depth_options = range(10, 251) # for DT
min_samples_split_options = range(2, 4)

# create a parameter grid: map the parameter names to the values that should be saved
param_grid_dt = dict(max_depth=max_depth_options, min_sample_split=min_samples_split_options) # for DT

# instantiate the grid
grid = GridSearchCV(dt, param_grid_dt, cv=10, scoring='accuracy')

# fit the grid with param
grid.fit(X, y)

'''# view complete results
grid.grid_scores_

# examine results from first tuple
print grid.grid_scores_[0].parameters
print grid.grid_scores_[0].cv_validation_scores
print grid.grid_scores_[0].mean_validation_score

# examine the best model
print '*******Final results*********'
print grid.best_score_
print grid.best_params_
print grid.best_estimator_'''

结果:

None
Traceback (most recent call last):
  File "C:\Users\KubiK\Desktop\GridSearch_ex6.py", line 31, in <module>
    grid.fit(X, y)
  File "C:\Users\KubiK\Anaconda2\lib\site-packages\sklearn\grid_search.py", line 804, in fit
    return self._fit(X, y, ParameterGrid(self.param_grid))
  File "C:\Users\KubiK\Anaconda2\lib\site-packages\sklearn\grid_search.py", line 553, in _fit
    for parameters in parameter_iterable
  File "C:\Users\KubiK\Anaconda2\lib\site-packages\sklearn\externals\joblib\parallel.py", line 800, in __call__
    while self.dispatch_one_batch(iterator):
  File "C:\Users\KubiK\Anaconda2\lib\site-packages\sklearn\externals\joblib\parallel.py", line 658, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\KubiK\Anaconda2\lib\site-packages\sklearn\externals\joblib\parallel.py", line 566, in _dispatch
    job = ImmediateComputeBatch(batch)
  File "C:\Users\KubiK\Anaconda2\lib\site-packages\sklearn\externals\joblib\parallel.py", line 180, in __init__
    self.results = batch()
  File "C:\Users\KubiK\Anaconda2\lib\site-packages\sklearn\externals\joblib\parallel.py", line 72, in __call__
    return [func(*args, **kwargs) for func, args, kwargs in self.items]
  File "C:\Users\KubiK\Anaconda2\lib\site-packages\sklearn\cross_validation.py", line 1520, in _fit_and_score
    estimator.set_params(**parameters)
  File "C:\Users\KubiK\Anaconda2\lib\site-packages\sklearn\base.py", line 270, in set_params
    (key, self.__class__.__name__))
ValueError: Invalid parameter min_sample_split for estimator DecisionTreeClassifier. Check the list of available parameters with `estimator.get_params().keys()`.
[Finished in 0.3s]

无
回溯（最近一次呼叫最后一次）：
文件“C:\Users\KubiK\Desktop\GridSearch\u ex6.py”，第31行，在
网格拟合（X，y）
文件“C:\Users\KubiK\Anaconda2\lib\site packages\sklearn\grid\u search.py”，第804行，fit
返回自拟合（X，y，参数网格（自参数网格））
文件“C:\Users\KubiK\Anaconda2\lib\site packages\sklearn\grid\u search.py”，第553行，in\u fit
对于参数_iterable中的参数
文件“C:\Users\KubiK\Anaconda2\lib\site packages\sklearn\externals\joblib\parallel.py”，第800行，在调用中__
而self.dispatch\u一批（迭代器）：
文件“C:\Users\KubiK\Anaconda2\lib\site packages\sklearn\externals\joblib\parallel.py”，第658行，分批发送
自我分配（任务）
文件“C:\Users\KubiK\Anaconda2\lib\site packages\sklearn\externals\joblib\parallel.py”，第566行，在调度中
作业=立即计算机批处理（批处理）
文件“C:\Users\KubiK\Anaconda2\lib\site packages\sklearn\externals\joblib\parallel.py”，第180行，在u init中__
self.results=batch（）
文件“C:\Users\KubiK\Anaconda2\lib\site packages\sklearn\externals\joblib\parallel.py”，第72行，在调用中__
返回[func（*args，**kwargs），用于self.items中的func、args、kwargs]
文件“C:\Users\KubiK\Anaconda2\lib\site packages\sklearn\cross\u validation.py”，第1520行，在_fit\u和_score中
估计量集参数（**参数）
文件“C:\Users\KubiK\Anaconda2\lib\site packages\sklearn\base.py”，第270行，在集合参数中
（键，self.\类\名\名）
ValueError:估计器DecisionTreeClassifier的最小样本分割参数无效。使用“estimator.get_params（）.keys（）”检查可用参数列表。
[以0.3秒完成]

您的代码中有一个输入错误，它应该是

minu samples\u split

而不是

minu samples\u split

您的代码中有一个输入错误，它应该是

min\u sample\u split

not

min\u sample\u split

您的代码应该是

min\u sample\u split

not

min\u sample\u split

您的代码应该是

min\u sample\u split