Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
Data Science
Towards Data Science·28d ago·5 min read
Your Model’s MSE Is Lying to You
TL;DR
Mean Squared Error (MSE) can be misleading when evaluating model performance, as two models can yield identical MSE but differ significantly in predictive reliability. Understanding the underlying distribution of predictions is crucial for making informed decisions.
✦ Why It Matters
Evaluate model predictions beyond MSE to improve decision-making in critical applications.
Key Takeaways
- Recognize that MSE can mask important uncertainties in predictions.
- Consider using probabilistic models to express prediction confidence.
- Shift from MSE to negative log-likelihood for better risk assessment.
- Understand that constant variance assumptions can lead to poor model performance.
How It Works
Instead of outputting a single predicted value, models should emit parameters of a probability distribution, such as the mean and standard deviation. This allows the model to express uncertainty and adapt its predictions based on the context of the data.