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Learner Reviews & Feedback for Estimating ML-Models Financial Impact by HSE University

About the Course

This course covers the basics of financial impact estimation for machine learning models deployed in business processes. We will discuss the general approaches to financial estimation, consider the applications to credit scoring and marketing response models, and focus on the relationship between statistical model quality metrics and financial results, as well as the concepts of A/B testing and potential biases as they apply to historical data. Multiple courses focus on building machine learning models and assessing their predictive power. However, much less attention is usually paid to explaining how the model quality translates into financial results. Even more so, decision strategies relying on model predictions are normally not covered in great detail. In this course, we will focus on the step when we already have a ML model and want to estimate the expected financial results, and verify the model by running either an A/B test or a backtest. In addition, we will learn how to tune threshold decision rules for model probabilities, thereby improving financial results, as well as account for model uncertainty or biases in historical data that may tamper with our financial estimates. We will analyze the binary classification case, which is the most common type of a ML task. After completing this course, you, as a data scientist, will be able to come up with better arguments when explaining the value of your machine learning models to your leadership. If your role in the company gravitates toward business processes, you will gain a better understanding of how machine learning models can have an impact on the financial results....
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1 - 2 of 2 Reviews for Estimating ML-Models Financial Impact

By Andrey B

Apr 22, 2021

Интересный курс, оригинальный материал, похожих не находил, при этом тема очень прикладная. Рекомендую.

By Vasilii D

Feb 3, 2021

Interesting course with useful information. Nonetheless, the course has a lot of opportunities to be better:

- superficial explanation of many financial terms

- Jupyter-notebooks code is negligible: its is raw and unworked

- case examples are limited to the banking sector

- the abhorrent quality of subtitles

- poor English level of some teachers (but not all)

- validator of programming assignments is not able to indicate which subtasks are invalid

Feel that the course was hastily conducted and did not pass the beta-testing phase. However, the course will benefit many data analysts in the banking sector. I believe that authors of the course are able to do great job.