質問編集履歴
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コードとエラーの詳細の追加
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エラーは以下の通りです。
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ValueError: Invalid parameter n_estimaters for estimator ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,
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max_features=0.1, max_leaf_nodes=None, min_impurity_decrease=0.0,
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min_impurity_split=None, 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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```python
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コード```
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import numpy as np
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import pandas as pd
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from sklearn.model_selection import GridSearchCV
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from sklearn.ensemble import ExtraTreesRegressor
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from sklearn.metrics import r2_score
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from sklearn.metrics import mean_squared_error
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from sklearn.metrics import mean_absolute_error
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sample_data = pd.read_csv('transformed_include_error_4.csv')
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sample_data = sample_data.query('Error ==0')
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sample_data = pd.read_csv('transformed_include_error_4.csv')
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test_data = sample_data.query('Error ==0')
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np.random.seed(seed=42)
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indices = np.random.permutation(len(sample_data))
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train_size = 140
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train_idx, test_idx = indices[:train_size], indices[train_size:]
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train_data = sample_data.iloc[train_idx]
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test_data = test_data.iloc[test_idx]
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y_train = train_data['Rate']
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x_train = train_data.loc[:,["feature_1","feature_2","feature_3","feature_4"]]
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y_test = test_data['Rate']
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x_test = test_data.loc[:,["feature_1","feature_2","feature_3","feature_4"]]
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params = {
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clf.fit(x_train,y_train)
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############################
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SVRではチューニングもテストデータの予測も行えたのでデータに間違いがあるとは思えません。
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ExtraTreesRegressorを初めて使うので根本的なミスなど原因が知りたいです。
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