# Training using Teach Label (Explore) > :::note You must have assigned the **Source - Read** and **Dataset - Review** permissions as an Automation Cloud user, or the **View sources** and **Review and annotate** permissions as a legacy user. ::: :::note You must have assigned the **Source - Read** and **Dataset - Review** permissions as an Automation Cloud user, or the **View sources** and **Review and annotate** permissions as a legacy user. ::: **Teach** is the second step in the **Explore** phase and its purpose is to show predictions for a label where the model is most confused if it applies or not. Like previous steps, we need to confirm if the prediction is correct or incorrect, and by doing so provide the model strong training signals. It is the most important label-specific training mode. :::note **Teach Label** is a training mode designed exclusively for annotating unreviewed messages. As such, the reviewed filter is disabled in this mode. ::: ## Key steps 1. Select **Teach Label** from the dropdown menu as shown in the following image. 2. Select the label you wish to train, where the default selection in **Teach** mode is to show unreviewed messages. 3. You will be presented with a selection of messages where the model is most confused as to whether the selected label applied or not. This means you should review the predictions and apply the label if they are correct, or apply other labels if they are incorrect. :::note * Predictions will range outwards from ~50% for data with no sentiment and 66% for data with sentiment enabled. * Make sure you apply all other labels that apply as well as the specific label you are focusing on. ::: ![Example of using the Teach Label option in the Explore tab.](https://dev-assets.cms.uipath.com/assets/images/ixp/ixp-example-of-using-the-teach-label-option-in-the-explore-tab-601332-307c0902.webp) You should use this training mode as required to boost the number of training examples for each label to above 25, so that the platform can then accurately estimate the performance of the label. The number of examples required for each label to perform well will depend on a number of factors. In the **Refine** phase we cover how to understand and improve the performance of each label. The platform will regularly recommend using **Teach Label** as a means of improving the performance of specific labels by providing more varied training examples that it can use to identify other instances in your dataset where the label should apply. ## Solutions for insufficient Teach examples You may find after **Discover** and **Shuffle** that some labels still have very few examples, and where **Teach Label** mode does not surface useful training examples. In this case, you are recommended to use the following training modes to provide the platform with more examples to learn from: ![This image depicts an example of Teach when it does not generate enough training examples.](https://dev-assets.cms.uipath.com/assets/images/ixp/ixp-this-image-depicts-an-example-of-teach-when-it-does-not-generate-enough-training-examples-327960-c5230ca3.webp) **Option 1 - Search** Searching for terms or phrases in **Explore** works the same as searching in **Discover**. One of two key differences is that in **Explore** you must review and annotate search results individually, rather than in bulk. You can search in **Explore** by simply typing in your search term in the search box at the top left of the page. ![Accessing Search in Explore.](https://dev-assets.cms.uipath.com/assets/images/ixp/ixp-accessing-search-in-explore-322561-76ca80f7.webp) However, too much Search can biasyour model which is something we want to avoid. Add no more than 10 examples per label in this training mode to avoid annotating bias. Make sure you also allow the platform time to retrain before going back to **Teach** mode. For more details, check [Training using Search in the Explore tab](https://docs.uipath.com/ixp/automation-cloud/latest/cm-user-guide/training-using-search-explore). **Option 2 - Label** Although training using **Label** is not one of the main steps outlined in the **Explore** phase, it can still be useful in this phase of training. In **Label** mode, the platform shows you messages where that label is predicted in descending order of confidence, that is, with the most confident predictions first and least confident at the bottom. ![Accessing Label training mode in Explore.](https://dev-assets.cms.uipath.com/assets/images/ixp/ixp-accessing-label-training-mode-in-explore-601357-94d44576.webp) However, it is only useful to review predictions that are not high-confidence, above 90%. This is because when the model is very confident, that is, above 90%, then by confirming the prediction you are not telling the model any new information, it is already confident that the label applies. Look for less confident examples further down the page if needed. Although, if predictions have high confidences and are wrong, then make sure to apply the correct labels, thus rejecting the incorrect predictions. ## Useful tips * If for a label there are multiple different ways of saying the same thing, for example, A, B, or C, make sure that you give the platform training examples for each way of saying it. If you give it 30 examples of A, and only a few of B and C, the model will struggle to pick up future examples of B or C for that label. * Adding a new label to a mature taxonomy may mean it has not been applied to previously reviewed messages. This then requires going back and teaching the model on new labels, using the **Missed label** function.