1. Tracking performance
Welcome back! Model performance is crucial in decision science, but choosing the right metric to assess it is just as important.
2. Model performance
Imagine you're evaluating two risk models. One does an excellent job of predicting which customers are likely to default. At the same time, the other is better at estimating how much a customer might default on if they don't pay - but it's less accurate when predicting the probability of default.
Which model is better? The answer depends on your goal. Are you prioritizing identifying risky customers or the potential financial impact?
3. Model metrics
Different metrics shine a light on different aspects of performance.
Some commonly used metrics include accuracy, precision, recall, F1-Score, Area under the curve, mean absolute error and mean absolute percent error.
Let's discuss them first and then see how to implement this in a dashboard.
4. Accuracy and precision
Accuracy is a simple metric. For a binary outcome like spam or not spam, it indicates what percentage of predictions your model got right.
Precision is a critical metric when evaluating model performance, especially when the cost of false positives is high. It tells us the proportion of predicted positives that are correct. For example, in fraud detection, a model with low precision could flag many legitimate transactions as fraudulent, wasting time and resources.
5. More metrics
Recall measures how well a model identifies actual positives. It's especially important when missing a positive case has serious consequences, such as detecting fraudulent transactions or life-threatening diseases. High recall ensures you catch as many true positives as possible.
Area under the curve is especially useful for classification models. It measures how well your model separates positive and negative classes, regardless of a specific cut-off threshold.
For continuous outcomes, metrics such as Mean Absolute Error (MAE) and Mean Percentage Error (MPE) are commonly used to evaluate model performance.
6. Dashboards are critical
Choosing the right metric for the right problem is a skill in itself. For instance, detecting rare diseases requires a different metric than forecasting product sales.
Once you've evaluated model performance using the right metrics, the next step is effectively communicating these insights through dashboards.
Dashboards transform complex analyses into clear, actionable insights, making it easier to drive decisions.
7. Basic principles
There are some basic principles for creating simple and effective dashboards.
Understand and Target Your Audience: Executives prefer high-level summaries, while analysts need more granular insights.
Focus on Key Metrics: What are the most important numbers? Resist the urge to cram everything in.
Clear Visualizations: Choose charts that match the data. Bar charts for comparisons between groups are helpful, and line graphs effectively show time trends. These simple charts are often more powerful than complex visuals.
8. More principles
Highlight Change Over Time: Is model performance slipping? Are key features drifting? Your dashboard should track trends.
Context Matters: Numbers alone mean little. Add brief text annotations to explain significant changes or patterns.
Get Feedback Early: Share with users. Do they understand the main message at a glance?
Lastly, remember to Iterate: Dashboards should evolve with your model. Make regular updates based on what's most useful.
Remember, a dashboard isn't just about pretty visuals. It's your tool to drive understanding, track progress, and trigger the right actions from your decision science insights!
9. Let's practice!
Ready to apply these principles? Let's dive into some hands-on exercises.