# Insights

## Insights Overview

**Tensorleap** insights could be viewed via the **insights** panel which is shown once you click on the **dashboard** view at the top. In order to see insights, a model must be loaded and evaluated or trained using the Tensorleap system.

## What are insights

Insights are essentially clusters in your model's latent space, that have unique properties that should be considered for the sake of model & dataset analysis.

Each Cluster is composed of a group of samples, that have some shared features, and a unique behaviour as detected by the TensorLeap platform. 

We detect 4 types of clusters:

* High-Loss Cluster - a collection of similar samples that the model performs poorly on.
* Overfitting Cluster - a collection of samples on which the model scored significantly better on the train subset than on the validation subset.
* Repetitive Cluster - a collection of samples, that have very low variance in features, compared to the rest of the samples.
* Underrepresentation Cluster -  a collection of samples that are composed of an uneven representation from the validation and train subsets, i.e. the cluster has a significantly higher number of samples that are from the training subset than the validation subset or vice versa. 

## Insights review

On the top-right of the insights panel the number of insights, per type, is shown. Clicking the **Display** button on the insights will refer to the display of the specific cluster.

<figure><img src="https://3509361326-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F9UXeOlFqlw8pl79U2HGU%2Fuploads%2FFZTWVJw6nNmfm5cdfWpO%2Fselecting%20a%20cluster.gif?alt=media&#x26;token=5d913808-18f7-456b-9e60-2f46c6ef916b" alt=""><figcaption><p>Zooming in to interesting clusters using Tensorleap insights</p></figcaption></figure>

Once we filter the relevant samples that make out an interesting cluster we can use Tensorleap [Sample Analysis](https://docs.tensorleap.ai/user-interface/project/dashboards/dashlets/sample-analysis) to better understand each sample and analyze the root cause for the cluster's behaviour.&#x20;