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examples/applications/porto_seguro_keras_under_sampling.py

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@@ -220,6 +220,12 @@ def fit_predict_balanced_model(X_train, y_train, X_test, y_test):
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import seaborn as sns
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import matplotlib.pyplot as plt
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sns.boxplot(y='level_0', x=0, data=df_time)
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sns.despine(top=True, right=True, left=True)
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plt.xlabel('time [s]')
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plt.ylabel('')
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plt.title('Computation time difference using a random under-sampling')
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sns.boxplot(y='level_0', x=0, data=df_results, whis=10.0)
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sns.despine(top=True, right=True, left=True)
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ax = plt.gca()
@@ -228,9 +234,3 @@ def fit_predict_balanced_model(X_train, y_train, X_test, y_test):
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plt.xlabel('ROC-AUC')
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plt.ylabel('')
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plt.title('Difference in terms of ROC-AUC using a random under-sampling')
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sns.boxplot(y='level_0', x=0, data=df_time)
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sns.despine(top=True, right=True, left=True)
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plt.xlabel('time [s]')
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plt.ylabel('')
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plt.title('Computation time difference using a random under-sampling')

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