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3

実行結果の追加

2018/07/13 05:05

投稿

lilium_09
lilium_09

スコア9

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@@ -152,4 +152,26 @@
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  ```Python
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  UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no predicted samples.
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  'precision', 'predicted', average, warn_for
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+ ```
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+
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+ ###追記3
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+ ```Python
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+ fit_time score_time test_accuracy test_f1_macro train_accuracy \
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+ 0 0.0005 0.001501 0.533333 0.347826 0.522727
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+ 1 0.0005 0.001001 0.522727 0.343284 0.528090
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+ 2 0.0005 0.000500 0.522727 0.343284 0.528090
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+
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+ train_f1_macro
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+ 0 0.343284
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+ 1 0.345588
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+ 2 0.345588
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+
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+
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+
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+ Z:\Downloads\WinPython-64bit-3.6.3.0Qt5\python-3.6.3.amd64\lib\site-packages\sklearn\metrics\classification.py:1135: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
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+ 'precision', 'predicted', average, warn_for)
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+ Z:\Downloads\WinPython-64bit-3.6.3.0Qt5\python-3.6.3.amd64\lib\site-packages\sklearn\metrics\classification.py:1135: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
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+ 'precision', 'predicted', average, warn_for)
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+ Z:\Downloads\WinPython-64bit-3.6.3.0Qt5\python-3.6.3.amd64\lib\site-packages\sklearn\metrics\classification.py:1135: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
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+ 'precision', 'predicted', average, warn_for)
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  ```

2

書式改善

2018/07/13 05:05

投稿

lilium_09
lilium_09

スコア9

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  Windows10
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  WinPython-64bit-3.6.3(Python3.6)
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- ###追記
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+ ###追記1
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  ```Python
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  fit_time score_time test_f1 test_precision test_recall train_f1 \
@@ -146,4 +146,10 @@
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  0 0.0 0.0
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  1 0.0 0.0
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  2 0.0 0.0
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+ ```
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+
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+ ###追記2
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+ ```Python
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+ UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no predicted samples.
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+ 'precision', 'predicted', average, warn_for
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  ```

1

ご指摘いただいた結果

2018/07/13 03:25

投稿

lilium_09
lilium_09

スコア9

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  ### 補足情報(FW/ツールのバージョンなど)
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  開発環境
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  Windows10
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- WinPython-64bit-3.6.3(Python3.6)
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+ WinPython-64bit-3.6.3(Python3.6)
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+
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+ ###追記
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+ ```Python
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+
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+ fit_time score_time test_f1 test_precision test_recall train_f1 \
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+ 0 0.001001 0.002001 0.0 0.0 0.0 0.0
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+ 1 0.001001 0.002001 0.0 0.0 0.0 0.0
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+ 2 0.000000 0.002002 0.0 0.0 0.0 0.0
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+
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+ train_precision train_recall
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+ 0 0.0 0.0
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+ 1 0.0 0.0
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+ 2 0.0 0.0
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+ ```