本在线课程涵盖了业务流程中部署的机器学习模型的财务影响估计的基础知识。内容涵盖：通用财务估算方法；信贷评分与营销响应模型实战；统计质量指标与盈利/亏损的对应关系；A/B 测试设计；历史数据偏差的识别与处理。
大多数课程侧重于构建机器学习模型并评估其预测能力。 然而，却鲜少关注解释模型质量如何转化为财务结果。 更罕有深入探讨依赖于模型预测的决策策略。
在本课程中，我们将重点介绍当我们已经拥有机器学习模型并希望估计预期财务结果时的步骤，并通过运行A/B测试或回测来验证模型。 此外，我们将学习如何调整模型概率的阈值决策规则，从而改善财务结果，以及考虑模型不确定性或历史数据中可能篡改我们的财务估计的偏差。 我们将分析二进制分类案例，这是机器学习任务中最常见的类型。
完成本课程后，作为数据科学家，您将能够在向领导层解释机器学习模型的价值时提出更好的论据。 如果您在公司中的角色倾向于业务流程，您将更好地了解机器学习模型如何对财务结果产生影响。
This online 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.
