.. _Metrics Tabular Classification GUI:

Metrics for the Tabular Classification model with GUI
======================================================

The **Metrics** tab calculates a set of metrics on the provided dataset. 

Metrics, provided for **Classification** are: 
   
.. math:: classification error = \frac{number\_of\_wrong\_classification}{number\_of\_samples}

.. math:: \frac{ \|prediction - reference\|_{fro}}{\|reference\|_{fro}}

.. math:: \frac{ \|prediction - reference\|_{fro}}{\|(reference - mean(reference))\|_{fro}}

.. math:: \frac{max(|prediction - reference|)}{max(|reference|)}

.. math:: \frac{max(|prediction - reference|)}{max(|reference|) - min(|reference|)}


* Switch to the **Metrics** tab

* To calculate metrics, click on the dataset in the **Evaluation files** section. Use **Aditional +** to add the datasets. 

* The results are displayed, and the **Metrics** tab provides also a **Confusion Matrix** for the selected dataset.

An example of a result looks as follows:

.. figure:: ../../../../images/GUIMetricsClassification.png
  :width: 600
  :alt: GUIMetricsClassification
  :align: center

  GUI operations: metrics evaluation for **Classification**

.. Note::
    
  | By default, the evaluation of metrics is performed with the last model available in the checkpoint.
  | Use the checkpoint slider in the bottom to choose any other available model and get its metrics.