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...
