# 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

<figure><img src="/files/LsmuTV7e6JaMJqiiVP83" alt=""><figcaption></figcaption></figure>

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.

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

## Insights Video Tutorial

{% embed url="<https://app.guidde.com/share/playbooks/wd44deRrBVBESEcbGAFcsd?mode=videoOnly&origin=k2buG3CvzZWUzfsWk7HPoOLDKpg2>" %}


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