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You cannot calculate a model’s current accuracy from its unlabeled predictions alone. You can estimate it by combining target predictions with labeled source data and an explicit assumption about how the target population differs. Treat the result as an estimate with uncertainty—not as observed accuracy. For consequential decisions, a representative labeled audit is the clearest way to check performance directly.
What can you know without target labels?
Accuracy is the share of predictions that match the true labels in a defined population. To calculate it directly, you need both the predictions and the corresponding correct answers. Unlabeled predictions do not reveal which ones are right: the same set of predictions could be correct for one set of hidden labels and wrong for another.
Unlabeled evaluation methods therefore add information or assumptions. They may draw on labeled source or validation examples, confidence scores, the outputs of multiple predictors, or a model of how the target data has shifted. Their estimates are conditional on those inputs and assumptions. A detected change in input data is evidence of drift to investigate, not a direct count of prediction errors.
Define the metric and population first
Before estimating anything, specify what “accurate” means for the decision at hand and which examples the estimate should represent. Accuracy is not interchangeable with precision, recall, F1, calibration error, or business utility; a method that estimates one does not automatically estimate the others.
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- Metric: Name the performance measure, classification setting, and any decision threshold it depends on.
- Population: Define the time period, geography, user group, or other population represented by the unlabeled target examples.
- Reference data: Identify the labeled source or validation set, when and how it was collected, and whether it resembles the target population.
- Shift assumption: Decide whether a particular kind of distribution change is plausible. If the model faces a new population or changed relationship between inputs and outcomes, estimates based on older data may be unreliable.
Which unlabeled estimation methods fit?
These approaches answer different questions and rely on different conditions. The relevant choice depends on the task, available labeled data, and plausible shift—not on a universal ranking.
| Method | Assumption or setting | Data needed beyond unlabeled target examples | Estimated quantity | Main caveat |
|---|---|---|---|---|
| Bayesian or multi-predictor estimation (Platanios, Dubey, and Mitchell, 2016) | A graphical-model approach; extensions can combine outputs from multiple classifiers. | Outputs from classifiers on unlabeled examples and the information required by the chosen model. | Classifier accuracy. | The paper reported estimates within a few percent of true accuracy on two real-world datasets in its tested setup; that result is not a general error guarantee. |
| Black Box Shift Estimation (BBSE; Lipton, Wang, and Smola, 2018) | Label shift: the label marginal changes while the input distribution conditional on each label remains fixed. | A predictor’s confusion matrix estimated using labeled source examples, plus its predictions on target examples. | Target label proportions, which can support performance estimation under the stated shift setup. | The stated guarantees require an invertible confusion matrix. It can use a biased, inaccurate, or uncalibrated predictor, but a better predictor can tighten estimates. |
| Average Thresholded Confidence (ATC; Garg and coauthors, ICLR 2022) | A confidence threshold learned on source data is transferred to target data. | Labeled source examples to fit the threshold and confidence scores for unlabeled target examples. | Target performance estimated from the share of target examples whose confidence exceeds the threshold. | The threshold’s transferability is an assumption. The paper reported 2–3 times more accurate estimates than recently proposed methods across its tested architectures, shifts, and datasets; this is not a deployment guarantee. |
| PAPE (NeurIPS, 2025) | Binary classification on unlabeled tabular data under covariate shift. | Source information and unlabeled target tabular examples as required by the method. | Performance metrics defined from confusion-matrix elements. | Its stated scope is specific to binary tabular classification under covariate shift. The paper reports evaluation on more than 900 dataset-model combinations using US census data; that does not establish performance on other tasks. |
| LaSCal | Label shift. | Source information and unlabeled target examples, as required for its calibration estimate. | Calibration error, which can inform post-hoc calibration on the target distribution. | Calibration error measures alignment between confidence scores and classifier accuracy; it is not the same as accuracy. |
| Prediction intervals for performance estimates (Elder and coauthors) | A transfer-learning approach evaluated across drift conditions. | Inputs required by the transfer-learning approach and the performance-estimation method. | An interval expressing uncertainty around a performance estimate. | An interval does not remove the underlying method’s assumptions or guarantee that the target population is represented. |
| Unsupervised error estimation (Donmez, Lebanon, and Balasubramanian) | Classification and regression error estimation under stated mild assumptions. | Unlabeled data and the assumptions required by the framework. | Classification or regression error. | Its consistency results apply under the framework’s assumptions; they do not make unlabeled error observable without assumptions. |
How to produce and report an estimate
- Freeze the question. Record the metric, target population, model version, prediction threshold, and time window. Otherwise, a changing mix of examples or model settings can make successive estimates incomparable.
- Check what labeled data remains. Find the source or validation labels used to characterize the model, including how representative they are of the target population. Some methods need a source confusion matrix; others need a source-fitted confidence threshold.
- Choose a method whose assumptions match the suspected shift. Use a label-shift approach only when its condition—that the input distribution conditional on the label stays fixed—is plausible. Consider a method designed for covariate shift only within its stated task scope. If the shift type is unclear, do not present a specialized method’s result as assumption-free.
- Apply it to target examples and preserve uncertainty. Report the estimate together with an interval when the method provides one, or otherwise describe the uncertainty and assumptions that affect it. Do not turn a point estimate into an exact-sounding accuracy claim.
- Validate against labels when the decision warrants it. Draw a representative target sample, obtain its labels, and calculate the metric directly. Compare that result with the unlabeled estimate to assess whether the method is useful for this deployment.
What confidence scores and drift alerts do—and do not—show
A high confidence score is not itself proof that a prediction is correct. ATC uses confidence as an input to an estimate by transferring a threshold learned on labeled source data; its usefulness depends on that transfer. Likewise, a shift detector can flag that target inputs differ from the data seen before, but input drift alone does not establish how many predictions are wrong.
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- Use scikit-learn to track an example ML project end to end
- Explore several models, including support vector machines, decision trees, random forests, and ensemble methods
- Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection
- Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers
- Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning
Calibration is another distinct property: it concerns whether predictions made at a given confidence level are correct at the corresponding frequency. A model can have a calibration issue without that quantity being identical to its accuracy. LaSCal estimates calibration error under label shift; it should not be described as a direct accuracy measurement.
When an unlabeled estimate is not enough
Use an estimate as a diagnostic when its assumptions are uncertain, especially if the model encounters a new population or the relationship between inputs and outcomes may have changed. Strong claims about current performance require ground-truth labels from the population being evaluated. Where decisions have serious consequences, prioritize a carefully selected, representative labeled audit rather than relying solely on an unlabeled estimate.
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