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Data Science Projects with Python - Second Edition

You're reading from Data Science Projects with Python - Second Edition

Product type Book

Published in Jul 2021

Publisher Packt

ISBN-13 9781800564480

Pages 432 pages

Edition 2nd Edition

Languages

Concepts

Author (1):

Stephen Klosterman

Table of Contents (9) Chapters

Preface

1. Data Exploration and Cleaning

2. Introduction to Scikit-Learn and Model Evaluation

3. Details of Logistic Regression and Feature Exploration

4. The Bias-Variance Trade-Off

5. Decision Trees and Random Forests

6. Gradient Boosting, XGBoost, and SHAP Values

7. Test Set Analysis, Financial Insights, and Delivery to the Client

Appendix

Financial Analysis

The model performance metrics we have calculated so far were based on abstract measures that could be applied to analyze any classification model: how accurate a model is, how skillful a model is at identifying true positives relative to false positives at different thresholds (ROC AUC), the correctness of positive predictions (precision), or intuitive measures such as sloping risk. These metrics are important for understanding the basic workings of a model and are widely used within the machine learning community, so it's important to understand them. However, for the application of a model to business use cases, we can't always directly use such performance metrics to create a strategy for how to use the model to guide business decisions or figure out how much value a model is expected to create. To go the extra mile and connect the mathematical world of predicted probabilities and thresholds to the business world of costs and benefits, a financial analysis...

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Authors (1)

Stephen Klosterman

Stephen Klosterman is a Machine Learning Data Scientist with a background in math, environmental science, and ecology. His education includes a Ph.D. in Biology from Harvard University, where he was an assistant teacher of the Data Science course. His professional experience includes work in the environmental, health care, and financial sectors. At work, he likes to research and develop machine learning solutions that create value, and that stakeholders understand. In his spare time, he enjoys running, biking, paddleboarding, and music.

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