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Exercise

Bringing it all together

One of the engineers in your arrhythmia detection startup rushes into your office to let you know that there is a problem with the ECG sensor for overweight users. You decide to reduce the influence of examples with weight over 80 by 50%. You are also told that since your startup is targeting the fitness market and makes no medical claims, scaring an athlete unnecessarily is costlier than missing a possible case of arrhythmia. You decide to create a custom loss that makes each "false alarm" ten times costlier than missing a case of arrhythmia. Does down-weighting overweight subjects improve this custom loss? Your training data X_train, y_train and test data X_test, y_test are preloaded, as are confusion_matrix(), numpy as np, and DecisionTreeClassifier().

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  • Start by creating a custom loss which extracts the false positives and false negatives from the confusion matrix, and then makes each false alarm count ten times as much as a missed case of arrhythmia.
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