質問編集履歴
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不均衡データを3つのクラスに分類し、グリッド
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不均衡データを3つのクラスに分類し、グリッドサーチをしたいのですが 上手くいきません。
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不均衡データを3つのクラスに分類し、グリッド
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不均衡データを3つのクラスに分類し、グリッドサーチをしたいのですが
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（例）PHP(CakePHP)で●●なシステムを作っています。
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■■な機能を実装中に以下のエラーメッセージが発生しました。
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### 発生している問題・エラーメッセージ
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```
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ValueError Traceback (most recent call last)
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<ipython-input-15-32a008acd626> in <module>
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41 grid_search = GridSearchCV(LinearSVC(class_weight="balanced"), param_grid, cv = 4)
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---> 43 grid_search.fit(X_train_std, y_train)
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44
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45 # 結果
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~\Anaconda3\lib\site-packages\sklearn\model_selection\_search.py in fit(self, X, y, groups, **fit_params)
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686 return results
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687
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--> 688 self._run_search(evaluate_candidates)
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689
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690 # For multi-metric evaluation, store the best_index_, best_params_ and
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~\Anaconda3\lib\site-packages\sklearn\model_selection\_search.py in _run_search(self, evaluate_candidates)
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1147 def _run_search(self, evaluate_candidates):
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1148 """Search all candidates in param_grid"""
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-> 1149 evaluate_candidates(ParameterGrid(self.param_grid))
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1150
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~\Anaconda3\lib\site-packages\sklearn\model_selection\_search.py in evaluate_candidates(candidate_params)
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665 for parameters, (train, test)
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666 in product(candidate_params,
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--> 667 cv.split(X, y, groups)))
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669 if len(out) < 1:
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~\Anaconda3\lib\site-packages\joblib\parallel.py in __call__(self, iterable)
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919 # remaining jobs.
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920 self._iterating = False
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--> 921 if self.dispatch_one_batch(iterator):
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922 self._iterating = self._original_iterator is not None
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923
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~\Anaconda3\lib\site-packages\joblib\parallel.py in dispatch_one_batch(self, iterator)
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757 return False
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758 else:
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--> 759 self._dispatch(tasks)
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760 return True
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~\Anaconda3\lib\site-packages\joblib\parallel.py in _dispatch(self, batch)
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714 with self._lock:
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715 job_idx = len(self._jobs)
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--> 716 job = self._backend.apply_async(batch, callback=cb)
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717 # A job can complete so quickly than its callback is
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718 # called before we get here, causing self._jobs to
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~\Anaconda3\lib\site-packages\joblib\_parallel_backends.py in apply_async(self, func, callback)
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180 def apply_async(self, func, callback=None):
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181 """Schedule a func to be run"""
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--> 182 result = ImmediateResult(func)
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183 if callback:
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184 callback(result)
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~\Anaconda3\lib\site-packages\joblib\_parallel_backends.py in __init__(self, batch)
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547 # Don't delay the application, to avoid keeping the input
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548 # arguments in memory
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--> 549 self.results = batch()
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550
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551 def get(self):
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~\Anaconda3\lib\site-packages\joblib\parallel.py in __call__(self)
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223 with parallel_backend(self._backend, n_jobs=self._n_jobs):
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224 return [func(*args, **kwargs)
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--> 225 for func, args, kwargs in self.items]
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227 def __len__(self):
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~\Anaconda3\lib\site-packages\joblib\parallel.py in <listcomp>(.0)
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223 with parallel_backend(self._backend, n_jobs=self._n_jobs):
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224 return [func(*args, **kwargs)
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--> 225 for func, args, kwargs in self.items]
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227 def __len__(self):
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~\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, error_score)
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501 train_scores = {}
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502 if parameters is not None:
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--> 503 estimator.set_params(**parameters)
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505 start_time = time.time()
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~\Anaconda3\lib\site-packages\sklearn\base.py in set_params(self, **params)
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222 'Check the list of available parameters '
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223 'with `estimator.get_params().keys()`.' %
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--> 224 (key, self))
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226 if delim:
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ValueError: Invalid parameter gamma for estimator LinearSVC(C=0.001, class_weight='balanced', dual=True, fit_intercept=True,
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intercept_scaling=1, loss='squared_hinge', max_iter=1000,
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```Python
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ソースコード
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