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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Designing Machine Learning Workflows in Python

AdvancedSkill Level

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In the previous chapters you established a solid foundation in supervised learning, complete with knowledge of deploying models in production but always assumed you a labeled dataset would be available for your analysis. In this chapter, you take on the challenge of modeling data without any, or with very few, labels. This takes you into a journey into anomaly detection, a kind of unsupervised modeling, as well as distance-based learning, where beliefs about what constitutes similarity between two examples can be used in place of labels to help you achieve levels of accuracy comparable to a supervised workflow. Upon completing this chapter, you will clearly stand out from the crowd of data scientists in confidently knowing what tools to use to modify your workflow in order to overcome common real-world challenges.

Exercise 1: Anomaly detection Exercise 2: A simple outlier Exercise 3: LoF contamination Exercise 4: Novelty detection Exercise 5: A simple novelty Exercise 6: Three novelty detectors Exercise 7: Contamination revisited Exercise 8: Distance-based learning Exercise 9: Find the neighbor Exercise 10: Not all metrics agree Exercise 11: Unstructured data Exercise 12: Restricted Levenshtein Exercise 13: Bringing it all together Exercise 14: Concluding remarks