質問編集履歴
2
提出完了報告
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min_samples_leaf=1, min_samples_split=2,
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min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,
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oob_score=False, random_state=None, verbose=0, warm_start=False). Check the list of available parameters with `estimator.get_params().keys()`.
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# kaggleのTitanic課題提出まで漕ぎ着けました!
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[hayataka2049](https://teratail.com/users/hayataka2049) さまのおかげです(*≧∀≦)
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`classifier__`削除後のエラー文追加
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- Windows7
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# 躓いた箇所とエラー文
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grid_param内の書き方がよくないのだと推測しています。もともとのTitanic課題kernelには`classifier__` はついていなかったのですが、[stackoverflow](https://stackoverflow.com/questions/34889110/random-forest-with-gridsearchcv-error-on-param-grid)を見て`classifier__`をつけることにしました。
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**↑`classifier__`削除済**
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し
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削除はしたものの、ExtraTreesClassifierでエラーが出ています。
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エラー文からは`BaggingClassifier`と`estimator.get_params().keys()`が関係しているように読めるのですが。。。
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```python
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#WARNING: Running is very computational intensive and time expensive.
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grid_n_estimator = [10, 50, 100, 300]
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[{
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'n_estimators': grid_n_estimator,
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'max_samples': grid_ratio,
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'random_state': grid_seed
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}],
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[{
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'n_estimators': grid_n_estimator,
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'criterion': grid_criterion,
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'max_depth': grid_max_depth,
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'random state': grid_seed
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}],
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[{
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'learning_rate': [.05],
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'n_estimators': [300],
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'max_depth': grid_max_depth,
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'random_state': grid_seed
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}],
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[{
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'n_estimators': grid_n_estimator,
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'criterion': grid_criterion,
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'max_depth': grid_max_depth,
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'oob_score': [True],
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'random_state': grid_seed
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}],
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[{
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'max_iter_predict': grid_n_estimator,
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'random_state': grid_seed
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}],
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[{
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'fit_intercept': grid_bool,
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'solver': ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'],
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'random_state': grid_seed
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}],
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[{
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'alpha': grid_ratio,
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}],
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[{}],
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[{
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'n_neighbors': [1,2,3,4,5,6,7],
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'weights': ['uniform', 'distance'],
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'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute']
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}],
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'C': [1,2,3,4,5],
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'gamma': grid_ratio,
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'decision_function_shape': ['ovo', 'ovr'],
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'probability': [True],
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'random_state': grid_seed
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}],
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[{
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'learning_rate': grid_learn,
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'max_depth': [1,2,4,6,8,10],
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'n_estimators': grid_n_estimator,
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'seed': grid_seed
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}]
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]
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print('-' *10)
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```
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**エラー文**
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**`classifier__`削除後のエラー文**
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```
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ValueError: Invalid parameter
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ValueError: Invalid parameter random state for estimator ExtraTreesClassifier(bootstrap=False, class_weight=None, criterion='gini',
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max_depth=2, max_features='auto', max_leaf_nodes=None,
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min_impurity_decrease=0.0, min_impurity_split=None,
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min_samples_leaf=1, min_samples_split=2,
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min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,
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oob_score=False, random_state=None, verbose=0, warm_start=False). Check the list of available parameters with `estimator.get_params().keys()`.
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```
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