> For the complete documentation index, see [llms.txt](https://docs.tensorleap.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.tensorleap.ai/getting-value-from-tensorleap/evaluate-unlabelled-data.md).
# Evaluate Unlabelled Data
This describes how to evaluate the performance of your model on unlabelled data
### **Why Unlabeled Evaluation Matters**
Many teams work with large unlabeled datasets — either as part of a data pipeline or when exploring new domains. But without labels, it's hard to assess whether that data contains edge cases, domain shifts, or problematic samples.
Tensorleap allows you to estimate model behavior on unlabeled data by computing **Predicted Metrics** — model-driven approximations of performance metrics like loss, IOU, confidence, and more.
This enables:
* Detecting potential errors or low-quality samples
* Surfacing outliers and high-loss examples
* Auditing incoming datasets before investing in labeling
### **How Tensorleap Helps Evaluate Unlabeled Data**
Once a model has been evaluated, Tensorleap computes predicted metrics on any connected unlabeled set. These estimates are derived from the model's internal signals — giving you visibility into likely performance without requiring ground truth.
You can:
* Sort and filter samples based on predicted loss, IOU, or confidence
* Visualize distributions to uncover outliers and edge cases
* Flag suspicious or low-confidence samples for further review or labeling
Predicting the loss for unlabeled samples for a NER dataset (yellow is higher)
---
# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.
## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation by asking a question.
Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:
```
GET https://docs.tensorleap.ai/getting-value-from-tensorleap/evaluate-unlabelled-data.md?ask=&goal=
```
`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.
Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.
