When the Model Says “Probably”: Confidence, Fallbacks, and Output Contracts

Magika does not treat the class with the highest neural score as automatically fit for an automated decision. This chapter examines the policy layer that stands between a raw model prediction and the result a caller should use: per-content-type thresholds, selectable prediction modes, generic fallbacks, and the distinction between dl and output. The practical judgment is straightforward but consequential: the raw score is evidence for diagnosis, while the final label is a calibrated tool decision whose meaning depends on the selected mode and model version.

A score is not equally persuasive for every type

A multi-class classifier normally produces one score for each possible class and selects the largest. That operation answers a narrow question: which learned class best matches the sampled representation? It does not answer whether the match is strong enough to trust. A score of 0.80 can mean different things for different classes because the classes have different distributions of examples and different neighboring confusions.

Magika's research documentation uses a useful contrast. Most valid PDFs in its experiments were detected with confidence above 99 percent, so an 80 percent PDF score could be questionable. An 80 percent score for JavaScript was often much more reliable. A single global cutoff would force these classes to share a standard that fits neither one well: set it low, and the PDF branch accepts too many false positives; set it high, and the JavaScript branch discards useful correct predictions.

The remedy is a threshold for each content type. During calibration, the system examines how precision and recall change as the cutoff moves. Precision asks how often a label is correct when the tool emits it; recall asks how much of the true class the tool successfully identifies. Raising a threshold usually makes a class's accepted predictions more selective, improving precision while reducing recall. Lowering it allows more files through, but also increases the chance that a visually plausible class is accepted when another class is more appropriate.

A per-content-type threshold lets that trade-off differ by class. It is not a declaration that one class is intrinsically more important than another. It is a response to the fact that the model's scores are not equally calibrated across the output space. In the initial research design, the authors describe choosing thresholds by fixing a target precision and then selecting as much recall as possible under that constraint. The current model configuration carries tuned thresholds for selected labels and a general medium-confidence threshold. The exact values belong to the specific model configuration, not to Magika as an eternal constant.

This calibration layer also protects the meaning of a generic result. Suppose the model's top class is a particular programming language, but its score falls below the threshold for that class. Magika can decline to expose that fine-grained label and return generic text instead. If the predicted class is treated as binary, it can return unknown binary data. The tool is not claiming that it has learned nothing. It is saying that the available evidence does not justify the most specific public answer.

That behavior is especially useful when the alternative is forced certainty. An unfamiliar text file may share punctuation and keywords with several supported languages. A short or unusual binary may resemble a known format at the sampled boundaries without containing enough evidence to distinguish it.