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An adaptive classifier updates its decision rule or parameters using information acquired while processing further data. In statistical classification, this can specialize an initially broad model to the conditions of a particular stream or document. The update may use supplied labels or examples accepted under a stated trust rule; adaptation is not evidence that every accepted label is correct.

In the 2007 description of Tesseract, satisfactory first-pass words provide examples for a more document-sensitive classifier. The paper motivates this by the limited collection of fonts within a document and describes normalization differences between static and adaptive classification. It explicitly does not describe the adaptive classifier as a simple template matcher.^[ref]

A separate toy calculation illustrates parameter adaptation without claiming to reproduce that implementation. Suppose a class is represented by the mean $\mu$ of one measured feature from $n$ trusted examples. Accepting another value $x$ givesWith $n=4$, $\mu =10$ and $x=12$, the updated mean is $10.4$. A classifier using that class mean can change its later decisions as a result.

The trust rule matters as much as the arithmetic. If a wrong prediction is accepted as a label, its update can make related wrong predictions more likely. A confidence score can help select examples, but a numerical score is not automatically a calibrated probability of correctness. A useful evaluation compares a frozen baseline and the adaptive system on checked data, including the possibility of degradation as well as improvement.

A confidence score is a numerical output used to express how strongly a system supports a prediction, particularly in statistical classification. Its meaning depends on the system: it may be an estimated probability, a distance from a decision boundary, or another score whose ordering is useful. A number between zero and one does not by itself establish a probability interpretation. Probability calibration concerns whether predicted probabilities agree with observed frequencies.^[ref]

Consider a synthetic binary statistical classification example. Among $100$ independent test cases assigned a positive-class score near $0.8$, suppose $80$ actually belong to that class. Their observed fraction $80/100=0.8$ agrees with the average predicted probability. If only $60$ belong to the positive class, the same scores overstate the observed positive frequency in this group. A reliability diagram groups predictions by their estimated probability and compares each group's mean prediction with its observed positive fraction. Finite groups have sampling variation, so agreement in one group is evidence to assess with its sample size, rather than a guarantee about each case.^[ref]

Ordering and probability calibration answer different questions. For scores $s$ in $[0,1]$, replacing every score by $s^3$ preserves their order but changes $0.8$ to $0.512$. Thus an unchanged ranking can accompany very different probability statements. Conversely, a system assigning the overall positive fraction to every case can have useful aggregate probability calibration while failing to distinguish cases.

A confidence score can support a threshold for accepting a prediction or passing it to another process. Select and assess that threshold using examples separate from model fitting and representative of the intended inputs. A threshold does not establish its own error rate; observations on suitable test data supply that evidence. Distinguish a positive-class probability from the score of whichever class was selected: they are different quantities when the selected class changes.