Ripasso delle metriche di prestazione

Ricordi il dataset del credito? Con tutte le conoscenze extra che ora hai sulle metriche, diamo un’altra occhiata a quanto sia valido un random forest su questo insieme di dati. Hai già addestrato il classificatore e ottenuto la matrice di confusione sui dati di test. I dati di test e i risultati sono disponibili come tp, fp, fn e tn, rispettivamente veri positivi, falsi positivi, falsi negativi e veri negativi. Hai anche le etichette di verità a terra per i dati di test, y_test, e le etichette previste, preds. Le funzioni f1_score() e precision_score() sono già state importate.

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Progettare workflow di Machine Learning in Python

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Progettare workflow di Machine Learning in Python

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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: Fusione dei dati Exercise 2: È la sorgente o la destinazione a essere compromessa?Exercise 3: Feature engineering su dati raggruppati Exercise 4: Etichette imperfette Exercise 5: Trasformare un'euristica in un classificatore Exercise 6: Combinare euristiche Exercise 7: Gestire il rumore nelle etichette Exercise 8: Funzioni di perdita Parte I Exercise 9: Ripasso delle metriche di prestazione

Esercizio in corso

Exercise 10: Analisi dei costi nel mondo reale Exercise 11: Calcoli con la matrice di confusione Exercise 12: Funzioni di perdita Parte II Exercise 13: Soglia predefinita Exercise 14: Ottimizzare la soglia Exercise 15: Mettere tutto insieme

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