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Exercise

Feedback loops

In real-world ML applications, it's not enough to just deploy a model and forget about it. As the data evolves, so should the model. The feedback loop is a way of ensuring that the model is continuously learning and adapting to changing data. Imagine that your heart disease model has been in production for a few months. As part of continuous monitoring and improvement, you want to assess the model's current performance and determine the need for potential retraining or adjustments. balanced_accuracy_score is imported for you from sklearn.metrics, ks_2samp is imported from scipy.stats, and two samples of the models true_labels_feb and predicted_labels_feb for the current time period have been predefined. Finally jan_data_samples and feb_data_samples have been loaded.

Instructions 1/3

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  • Compute and print the balanced accuracy of the model, comparing it to the prior month's balanced accuracy.