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Estimating ML-Models Financial Impact

Approx. 19 hours to complete

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Course Summary

Learn how to estimate the financial impact of machine learning models in this course. Discover different methods for cost estimation, measuring financial performance, and optimizing models for financial impact.

Key Learning Points

Understand the financial impact of machine learning models
Learn different methods for cost estimation and measuring financial performance
Optimize machine learning models for financial impact

Learning Outcomes

Estimate the financial impact of machine learning models
Measure financial performance of machine learning models
Optimize machine learning models for financial impact

Prerequisites or good to have knowledge before taking this course

Basic knowledge of machine learning concepts
Familiarity with Python programming language

Course Difficulty Level

Intermediate

Course Format

Online
Self-paced

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Applied Data Science: Machine Learning
Data Science: Machine Learning
Machine Learning with Python

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Description

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.

Knowledge

Estimating ML models financial impact for binary classification models
Design and evaluation of A/B tests
Reject inference

Outline

Project valuation: valuation metrics, planning and rules
Promo
Project valuation: NPV and other valuation criteria
Valuation planning
Expenses and income sources
Practice: Project valuation
Presentation: Project valuation
Project valuation

Model quality and decision making. Benefit curve
Simple threshold decisions
Benefit curves
Benefit curve and model quality metrics
Functional threshold decisions
Increment based threshold decisions
Practice: Benefit curve (credit)
Practice: Increment based threshold decisions (response)
Presentation: Model quality and decision making
Benefit curve

Estimating model risk discounts
What is Model Risk?
Consideration of model risk in income assessment
Methods of assessing model quality degradation over time
Calculation of model risk
Practice: Model risk (credit)
Practice: Model quality degradation (response)
Presentation: Model risk
Model risk

A/B testing and financial result verification
What is A/B testing?
5 principles of A/B testing
Preparing an A/B test
Randomization checking
Evaluation of A/B testing results
Practice: A/B testing
Presentation: A/B testing
A/B testing

Unobservable model errors, metalearning
Unobservable model errors
Control groups in A/B testing
Reject inference and semi-supervised learning
Expected benefit in case of unobserved FP and FN
Metalearning
Practice: Reject inference and metalearning
Practice: Reject inference and metalearning
Practice: Reject inference and metalearning
Presentation: Unobservable model errors & metalearning
Unobservable model errors

Summary of User Reviews

Learn how to estimate the financial impact of machine learning models in this Coursera course. Users appreciate the practical applications and real-world examples provided in the course.

Key Aspect Users Liked About This Course

Real-world examples and practical applications

Pros from User Reviews

Course provides practical applications for estimating financial impact of ML models
Real-world examples are used to illustrate concepts and techniques
Instructors are knowledgeable and responsive to questions
Course is well-structured and easy to follow

Cons from User Reviews

Course may be too basic for those with advanced knowledge of ML
Some users found the course content to be repetitive
Lack of hands-on exercises may make it difficult to fully grasp the concepts
Course may not provide enough depth for users looking for more advanced techniques

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