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

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Published inJul 2021

Reading LevelIntermediate

PublisherPackt

ISBN-139781800564480

Edition2nd Edition

Languages

Python

Concepts

Data Science

Author (1)

Stephen Klosterman

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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Data Science Projects with Python - Second Edition

Published in: Jul 2021Publisher: PacktISBN-13: 9781800564480

Author (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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Financial Analysis

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