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chore: run prettier
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@ -179,7 +179,7 @@ classifier.fit(X_train_scaled, y_train)
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![alt text][logo_ex4]
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[logo_ex4]: ./w2_day4_ex4_q3.png 'ROC AUC '
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[logo_ex4]: ./w2_day4_ex4_q3.png "ROC AUC "
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- https://scikit-learn.org/stable/modules/generated/sklearn.metrics.plot_roc_curve.html
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@ -115,7 +115,7 @@ array([[37, 2],
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![alt text][logo_ex4]
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[logo_ex4]: ../w2_day4_ex4_q3.png 'ROC AUC '
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[logo_ex4]: ../w2_day4_ex4_q3.png "ROC AUC "
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Having a 99% ROC AUC is not usual. The data set we used is easy to classify. On real data sets, always check if there's any leakage while having such a high ROC AUC score.
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