We solve the "Semantic Gap" between product requirements and ML implementation. If you can define the shape of the answer, the model builds itself.
📏The Universal Translator
Too often, projects stall because the requirement is vague ("I want AI") rather than structural ("I want a Classifier"). We deconstruct the three fundamental output primitives:
- Classification: Output is a label from a fixed list (Discrete).
- Regression: Output is a value on a sliding scale (Continuous).
- Clustering: Output is a coordinate in a vector space (Structure).
🏷️Classification Awareness
A classifier doesn't return a string; it returns a probability distribution (Softmax).
Dev Tip: Always implement thresholding. Don't trust the argmax. A 51% confidence is functionally "Unknown."
🕸️Clustering & Embeddings
When you don't have labels, you look for structure. "Vector Space" logic allows for semantic search, enabling math like King - Man + Woman = Queen.
🎯Key Takeaways
- •Classification gives you a discrete Label.
- •Regression gives you a continuous Value.
- •Metric Trap: Accuracy is vanity. Precision and Recall are sanity.
- •Embeddings allow you to treat meaning as geometry.
About Sandboxed
Sandboxed is a podcast for people who actually ship iOS apps and care about how secure they are in the real world.
Each episode, we take one practical security topic—like secrets, auth, or hardening your build chain—and walk through how it really works on iOS, what can go wrong, and what you can do about it this week.