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トップに関する質問[kaggle写経] BaggingClassifierでValueError

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質問編集履歴

2

提出完了報告

2018/09/12 03:37

投稿

Yukiya025
Yukiya025

スコア86

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@@ -249,3 +249,11 @@
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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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+ # 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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+ ![Titanic Submit](f7627fce2914e4a7fe863a1952635704.png)

1

`classifier__`削除後のエラー文追加

2018/09/12 03:37

投稿

Yukiya025
Yukiya025

スコア86

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@@ -18,11 +18,11 @@
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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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-
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- しかし、`BaggingClassifier`に関して、どう修正すべきなのかがわかりません(>_<)
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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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-
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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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@@ -64,21 +64,21 @@
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  'n_estimators': grid_n_estimator,
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- 'classifier__max_samples': grid_ratio,
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+ 'max_samples': grid_ratio,
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-
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- 'random_state': grid_seed
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+ 'random_state': grid_seed
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-
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- }],
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+ }],
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-
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-
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- [{
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+ [{
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-
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- 'n_estimators': grid_n_estimator,
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+ 'n_estimators': grid_n_estimator,
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-
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+
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- 'classifier__criterion': grid_criterion,
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+ 'criterion': grid_criterion,
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-
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+
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- 'classifier__max_depth': grid_max_depth,
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+ 'max_depth': grid_max_depth,
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  'random state': grid_seed
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@@ -92,23 +92,23 @@
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  'n_estimators': [300],
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- 'classifier__max_depth': grid_max_depth,
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+ 'max_depth': grid_max_depth,
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-
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- 'random_state': grid_seed
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+ 'random_state': grid_seed
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-
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+
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- }],
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+ }],
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+
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- [{
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+ [{
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-
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- 'n_estimators': grid_n_estimator,
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+ 'n_estimators': grid_n_estimator,
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-
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+
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- 'classifier__criterion': grid_criterion,
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+ 'criterion': grid_criterion,
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-
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+
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- 'classifier__max_depth': grid_max_depth,
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+ 'max_depth': grid_max_depth,
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-
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- 'classifier__oob_score': [True],
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+ 'oob_score': [True],
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  'random_state': grid_seed
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@@ -118,29 +118,29 @@
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  [{
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- 'classifier__max_iter_predict': grid_n_estimator,
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+ 'max_iter_predict': grid_n_estimator,
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-
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- 'random_state': grid_seed
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+ 'random_state': grid_seed
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-
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- }],
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+ }],
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- [{
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+ [{
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-
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- 'classifier__fit_intercept': grid_bool,
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+ 'fit_intercept': grid_bool,
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-
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+
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- 'classifier__solver': ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'],
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+ 'solver': ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'],
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-
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- 'random_state': grid_seed
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+ 'random_state': grid_seed
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-
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- }],
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+ }],
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- [{
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+ [{
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- 'classifier__alpha': grid_ratio,
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+ 'alpha': grid_ratio,
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  }],
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@@ -152,9 +152,9 @@
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  [{
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- 'classifier__n_neighbors': [1,2,3,4,5,6,7],
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+ 'n_neighbors': [1,2,3,4,5,6,7],
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-
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+
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- 'classifier__weights': ['uniform', 'distance'],
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+ 'weights': ['uniform', 'distance'],
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  'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute']
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@@ -164,13 +164,13 @@
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  [{
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- 'classifier__C': [1,2,3,4,5],
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+ 'C': [1,2,3,4,5],
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- 'classifier__gamma': grid_ratio,
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+ 'gamma': grid_ratio,
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- 'classifier__decision_function_shape': ['ovo', 'ovr'],
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+ 'decision_function_shape': ['ovo', 'ovr'],
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-
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+
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- 'classifier__probability': [True],
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+ 'probability': [True],
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  'random_state': grid_seed
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@@ -182,11 +182,11 @@
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  'learning_rate': grid_learn,
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- 'classifier__max_depth': [1,2,4,6,8,10],
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+ 'max_depth': [1,2,4,6,8,10],
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-
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- 'n_estimators': grid_n_estimator,
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+ 'n_estimators': grid_n_estimator,
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- 'classifier__seed': grid_seed
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+ 'seed': grid_seed
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  }]
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@@ -230,18 +230,22 @@
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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 classifier for estimator BaggingClassifier(base_estimator=None, bootstrap=True,
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+ ValueError: Invalid parameter random state for estimator ExtraTreesClassifier(bootstrap=False, class_weight=None, criterion='gini',
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-
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- bootstrap_features=False, max_features=1.0, max_samples=0.25,
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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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- n_estimators=300, n_jobs=1, oob_score=False, random_state=0,
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+ min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,
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-
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+
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- verbose=0, warm_start=False). Check the list of available parameters with `estimator.get_params().keys()`.
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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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  ```