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  3. Designing Machine Learning Workflows in Python

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().

Instructions 1/3

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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.