Berechnungen mit der Konfusionsmatrix

Dein Klassifikator auf den Kreditdaten erzielte folgende Werte: 163 True Positives, 15 False Positives, 48 False Negatives und 24 True Negatives. Diese Zahlen sind in der Konsole bereits als tp, fp, fn und tn für dich geladen. Die folgenden Aussagen beziehen sich auf zwei Metriken: Accuracy (Anteil korrekt klassifizierter Beispiele) und Recall (Anteil der tatsächlich positiven Beispiele, die als positiv klassifiziert wurden). Welche Aussage ist richtig?

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<Kurs>Machine-Learning-Workflows in Python entwerfen</Kurs>

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<Kurs>Machine-Learning-Workflows in Python entwerfen</Kurs>

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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.

Exercise 1: Supervised learning pipelines Exercise 2: Feature engineering Exercise 3: Your first pipeline Exercise 4: Model complexity and overfitting Exercise 5: Grid search CV for model complexity Exercise 6: Number of trees and estimators Exercise 7: Feature engineering and overfitting Exercise 8: Categorical encodings Exercise 9: Feature transformations Exercise 10: Bringing it all together

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.

Exercise 1: Datenfusion Exercise 2: Ist die Quelle oder das Ziel schlecht?Exercise 3: Feature Engineering auf gruppierten Daten Exercise 4: Unvollständige Labels Exercise 5: Eine Heuristik in einen Klassifikator verwandeln Exercise 6: Heuristiken kombinieren Exercise 7: Umgang mit Label-Rauschen Exercise 8: Loss-Funktionen Teil I Exercise 9: Erinnerung: Leistungsmetriken Exercise 10: Kostenanalyse in der Praxis Exercise 11: Berechnungen mit der Konfusionsmatrix

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Exercise 12: Loss Functions Teil II Exercise 13: Standard-Thresholding Exercise 14: Den Schwellenwert optimieren Exercise 15: Alles zusammenführen

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.

Exercise 1: From workflows to pipelines Exercise 2: Your first pipeline - again!Exercise 3: Custom scorers in pipelines Exercise 4: Model deployment Exercise 5: Pickles Exercise 6: Custom function transformers in pipelines Exercise 7: Iterating without overfitting Exercise 8: Challenge the champion Exercise 9: Cross-validation statistics Exercise 10: Dataset shift Exercise 11: Tuning the window size Exercise 12: Bringing it all together

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