Comparisons
Pairs that get conflated in real metric reviews and real design docs — batch and online, precision and recall, data drift and concept drift, validation and test. Neither column wins; what decides is the problem. Each record leads with the confusion, because the confusion is the reason the record exists.
Precision vs recall
They are treated as two scores of the same model when they are two ends of one threshold. Every classifier that outputs a score can be made high-precision or high-recall by moving the cut-off; reporting one without the other and without the threshold is reporting half a trade-off. The second confusion is F1, which averages them as if a false positive and a false negative cost the same — they almost never do, and the harmonic mean hides the asymmetry the business actually cares about. The strongest form of the precision side is operational: the flags go to a human queue with a fixed capacity, and every false positive displaces a real case, so precision at the queue size is the number. The strongest form of the recall side is that the cost of a miss is unbounded — a single missed case can be the incident — so the threshold must be set by recall and precision is whatever it then is. Neither is right in general; the right one is the one whose mistake is dearer, and the honest deliverable is the precision–recall curve with the operating point marked and priced.
When a false positive is the expensive mistake: a blocked legitimate payment, a customer emailed about fraud they did not commit, a page taken down. Each flag triggers an action with a cost, and the action budget is limited.
When a false negative is the expensive mistake: a missed tumour, a missed fraud, a missed outage. Missing one costs more than reviewing ten, and there is capacity to review the extra flags.
| Dimension | Precision — of the cases the model flagged, how many were real | Recall — of the real cases, how many the model flagged |
|---|---|---|
| Denominator | Predicted positives | Actual positives |
| Punishes | False positives | False negatives |
| Rises when the threshold | Goes up | Goes down |
| Business reading | "How often is a flag real?" | "How many real cases do we catch?" |
| Operational constraint it fits | A review queue of fixed size | A cost of missing that dwarfs review |
| Degenerate way to max it | Flag one sure case | Flag everything |
| Combined view | Precision at k, or at a recall floor | Recall at a precision floor |
| What F1 hides | That the two costs differ | That the two costs differ |
Model families compared
Linear, trees, boosting, neural and k-NN — compared without naming a winner, and with the block that says where the comparison stops being true.
No column is a winner. Each family is a set of assumptions about the data — linear separability, axis-aligned interactions, additive residuals, a representation that can be learned, locality in feature space — and the right one is the one whose assumptions your data happens to satisfy at the size you have. The reflex answer “XGBoost” is the red flag this table exists to catch: it is often right on tabular data and it is never right as a reflex, because it skips the baseline that would have told you whether anything more than a linear model was needed. The where this comparison misleads block on every row is the part worth reading.
Count independent entities, not rows — a million events from ten thousand users is ten-thousand-sized data for generalising to new users. And the neural column flips completely with transfer learning: a pretrained model fine-tuned on two thousand images beats every other column on that task.
The "boosting wins on tabular" claim is true for a tuned model on tens of thousands of clean rows with a validation set. It is not true for three hundred rows, for a problem whose signal is linear, for a regulator who wants coefficients, or for a team with no time to tune — and the gap to the linear model is often inside the error bar.
The columns are not competing on the same input. Once a pretrained network has produced an embedding, a linear model or k-NN on top of it is often within a few points of full fine-tuning — so "neural for images" usually means "a neural representation, then whichever head is cheapest".
Interpretability is not one property. A linear model with two hundred correlated features and L1 selection is harder to explain honestly than a depth-three tree; and every column's "explanation" is undermined equally by a leaked or proxy feature, which the explanation will present with confidence.
Training cost is dominated by the number of runs, not the run: a boosted model tuned over two hundred configurations costs more than a network fine-tuned once. And the k-NN column's zero is a loan repaid on every query.
The model is rarely the slow part. Feature retrieval, a network hop and JSON serialisation usually dwarf any of these numbers, so a latency budget is a question about the serving path before it is a question about the family.
A low burden on the modelling side does not remove the burden on the serving side: every column's features must be reproduced at prediction time with the same code, the same freshness and the same point-in-time semantics. Trees remove the need to engineer features, not the need to serve them.
Native handling is convenient and dangerous in equal measure: it lets a tree model learn that "missing" predicts the target, which is fine until serving produces missingness for a different reason — a timeout, a new form — and the model reads the outage as a signal.
Calibration only matters if a downstream decision multiplies the score by a cost or compares it to a probability threshold; a pure ranker does not need it. And calibration measured on the validation set drifts with the base rate in production, for every column alike.
Neither behaviour is "correct". A tree that predicts last year's maximum for a record-breaking day is wrong; a linear model that predicts a negative price is wrong differently. The honest answer is a monitor on inputs outside the training range and a fallback for them, whichever family serves.
Irrelevant is not the danger — leaky is. Every column will seize a feature that carries the answer, and the more capable the model the more efficiently it does so; robustness to noise says nothing about robustness to leakage, which only a point-in-time audit provides.
Every column is wrong somewhere, and the question that finds where is the same for all of them: how much data, of what modality, at what latency, with what interpretability requirement, judged by which metric, at what cost to train and serve. A model family chosen before those are answered is a guess with a library name.