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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().

This exercise is part of the course

Designing Machine Learning Workflows in Python

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Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Create a scorer assigning more cost to false positives
def my_scorer(y_test, y_est, cost_fp=10.0, cost_fn=1.0):
    tn, fp, fn, tp = ____
    return ____
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