我正在学习如何使用scikit-learn中的GridSearchCV()方法来寻找决策树分类器的最优超参数。
问题是，如果我只指定一个参数的选项，一切正常，如下所示：
print(__doc__)# Code source: Gael Varoquaux# Modified for documentation by Jaques Grobler# License: BSD 3 clausefrom sklearn import datasetsfrom sklearn.grid_search import GridSearchCVfrom sklearn.tree import DecisionTreeClassifier# define classifierdt = DecisionTreeClassifier()# import some data to play withiris = datasets.load_iris()X = iris.data[:, :2] # we only take the first two features.y = iris.target# define parameter values that should be searchedmin_samples_split_options = range(2, 4)# create a parameter grid: map the parameter names to the values that should be savedparam_grid_dt = dict(min_samples_split= min_samples_split_options) # for DT# instantiate the gridgrid = GridSearchCV(dt, param_grid_dt, cv=10, scoring='accuracy')# fit the grid with paramgrid.fit(X, y)# view complete resultsgrid.grid_scores_'''# examine results from first tupleprint grid.grid_scores_[0].parametersprint grid.grid_scores_[0].cv_validation_scoresprint grid.grid_scores_[0].mean_validation_score'''# examine the best modelprint '*******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，当我添加另一个参数的选项时，会出现“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 clausefrom sklearn import datasetsfrom sklearn.grid_search import GridSearchCVfrom sklearn.tree import DecisionTreeClassifier# define classifierdt = DecisionTreeClassifier()# import some data to play withiris = datasets.load_iris()X = iris.data[:, :2] # we only take the first two features.y = iris.target# define parameter values that should be searchedmax_depth_options = range(10, 251) # for DTmin_samples_split_options = range(2, 4)# create a parameter grid: map the parameter names to the values that should be savedparam_grid_dt = dict(max_depth=max_depth_options, min_sample_split=min_samples_split_options) # for DT# instantiate the gridgrid = GridSearchCV(dt, param_grid_dt, cv=10, scoring='accuracy')# fit the grid with paramgrid.fit(X, y)'''# view complete resultsgrid.grid_scores_# examine results from first tupleprint grid.grid_scores_[0].parametersprint grid.grid_scores_[0].cv_validation_scoresprint grid.grid_scores_[0].mean_validation_score# examine the best modelprint '*******Final results*********'print grid.best_score_print grid.best_params_print grid.best_estimator_'''结果：
NoneTraceback (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]回答：
你的代码中有一个拼写错误，应该是min_samples_split而不是min_sample_split。
