unorderable types：OneVsRest Classifierを実行しているときにdict（）<= int（）

Question

330フィーチャーと約800レコードの入力データに対してマルチラベル分類を実行しています。私は、次のparam_gridでRandomForestClassifierを活用しています：私は分類器を設定し、decision_fucntionモデルに適合し、適用していますどのように

> param_grid = {"n_estimators": [20], 
>    "max_depth": [6], 
>    "max_features": [80, 150], 
>    "min_samples_leaf": [1, 3, 10], 
>    "bootstrap": [True, False], 
>    "criterion": ["gini", "entropy"], 
>    "oob_score": [True, False]} 

は、データをクリーンアップした後、これは次のとおりです。

classifier = OneVsRestClassifier(RandomForestClassifier(param_grid)) 
y_score = classifier.fit(X_train, y_train).descition_function(X_test) 

X_train形状を - （ 800,334）、Y_train shape - （800,4）。分類の 数 - 4は、以下のエラーメッセージに走っ、0.18

しかしsklearnでコードを実行：

--------------------------------------------------------------------------- 
TypeError         Traceback (most recent call last) 
<ipython-input-164-db76d3122db8> in <module>() 
     1 classifier = OneVsRestClassifier(RandomForestClassifier(param_grid)) 
----> 2 y_score = classifier.fit(X_train, y_train).descition_function(X_test) 
     3 #clf = RandomForestClassifier() 
     4 #gr_search = grid_search.GridSearchCV(clf, param_grid02, cv=10, scoring = 'accuracy') 

/Users/ayada/anaconda/lib/python3.5/site-packages/sklearn/multiclass.py in fit(self, X, y) 
    214     "not %s" % self.label_binarizer_.classes_[i], 
    215     self.label_binarizer_.classes_[i]]) 
--> 216    for i, column in enumerate(columns)) 
    217 
    218   return self 

/Users/ayada/anaconda/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py in __call__(self, iterable) 
    756    # was dispatched. In particular this covers the edge 
    757    # case of Parallel used with an exhausted iterator. 
--> 758    while self.dispatch_one_batch(iterator): 
    759     self._iterating = True 
    760    else: 

/Users/ayada/anaconda/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py in dispatch_one_batch(self, iterator) 
    606     return False 
    607    else: 
--> 608     self._dispatch(tasks) 
    609     return True 
    610 

/Users/ayada/anaconda/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py in _dispatch(self, batch) 
    569   dispatch_timestamp = time.time() 
    570   cb = BatchCompletionCallBack(dispatch_timestamp, len(batch), self) 
--> 571   job = self._backend.apply_async(batch, callback=cb) 
    572   self._jobs.append(job) 
    573 

/Users/ayada/anaconda/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py in apply_async(self, func, callback) 
    107  def apply_async(self, func, callback=None): 
    108   """Schedule a func to be run""" 
--> 109   result = ImmediateResult(func) 
    110   if callback: 
    111    callback(result) 

/Users/ayada/anaconda/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py in __init__(self, batch) 
    320   # Don't delay the application, to avoid keeping the input 
    321   # arguments in memory 
--> 322   self.results = batch() 
    323 
    324  def get(self): 

/Users/ayada/anaconda/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py in __call__(self) 
    129 
    130  def __call__(self): 
--> 131   return [func(*args, **kwargs) for func, args, kwargs in self.items] 
    132 
    133  def __len__(self): 

/Users/ayada/anaconda/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py in <listcomp>(.0) 
    129 
    130  def __call__(self): 
--> 131   return [func(*args, **kwargs) for func, args, kwargs in self.items] 
    132 
    133  def __len__(self): 

/Users/ayada/anaconda/lib/python3.5/site-packages/sklearn/multiclass.py in _fit_binary(estimator, X, y, classes) 
    78  else: 
    79   estimator = clone(estimator) 
---> 80   estimator.fit(X, y) 
    81  return estimator 
    82 

/Users/ayada/anaconda/lib/python3.5/site-packages/sklearn/ensemble/forest.py in fit(self, X, y, sample_weight) 
    281 
    282   # Check parameters 
--> 283   self._validate_estimator() 
    284 
    285   if not self.bootstrap and self.oob_score: 

/Users/ayada/anaconda/lib/python3.5/site-packages/sklearn/ensemble/base.py in _validate_estimator(self, default) 
    94   """Check the estimator and the n_estimator attribute, set the 
    95   `base_estimator_` attribute.""" 
---> 96   if self.n_estimators <= 0: 
    97    raise ValueError("n_estimators must be greater than zero, " 
    98        "got {0}.".format(self.n_estimators)) 

TypeError: unorderable types: dict() <= int() 

Answer 1

なぜあなたは、パラメータのグリッドにRandomForestClassifierを初期化しようとしていますか？

あなたはグリッド検索をしたい場合は - ここでは例を見て： http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV