Projetando Workflows de Machine Learning em Python

In this chapter, you will be reminded of the basics of a supervised learning workflow, complete with model fitting, tuning and selection, feature engineering and selection, and data splitting techniques. You will understand how these steps in a workflow depend on each other, and recognize how they can all contribute to, or fight against overfitting: the data scientist's worst enemy. By the end of the chapter, you will already be fluent in supervised learning, and ready to take the dive towards more advanced material in later chapters.

In the previous chapter, you perfected your knowledge of the standard supervised learning workflows. In this chapter, you will critically examine the ways in which expert knowledge is incorporated in supervised learning. This is done through the identification of the appropriate unit of analysis which might require feature engineering across multiple data sources, through the sometimes imperfect process of labeling examples, and through the specification of a loss function that captures the true business value of errors made by your machine learning model.

In the previous chapter, you employed different ways of incorporating feedback from experts in your workflow, and evaluating it in ways that are aligned with business value. Now it is time for you to practice the skills needed to productize your model and ensure it continues to perform well thereafter by iteratively improving it. You will also learn to diagnose dataset shift and mitigate the effect that a changing environment can have on your model's accuracy.

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.