# Creating a report¶

Once learning is completed, models can be inspected by generating a **report** with the Report button

This will open a new window for selecting models. As many models as undisposed ones will be displayed.

Table of contents

## Model selection¶

Following the identification structure, models are organized in boots and starts in the left part of the window. Each boot has a dedicated tab with as many sections as random starts were selected, containing all undisposed models. For each model, the training and validation mean-squared errors (MSE) are shown.

Note

If the learning process was stopped by user, some starts and/or boots might be orphan as models where never generated.

As many models as desired can be selected/unselected from each available boot and/or start manually by using the checkbox. For a faster selection and/or deselection, each boot has the options Select all and Unselect all. This action propagates only to the current selected boot.

## Model preselection¶

Model preselection (eventually combined with a manual model selection in a second step) is a useful feature, particularly if a high number of boots and/or starts are available. Model preselection is applied by default, when the report model selection window is shown.

Preselection is performed by options displayed in the right part of the window

### All/None¶

Independently of other Model preselection fields, when clicking Preselect none (or Preselect all), none (or all)
available models (from **all boots/starts**) will be selected. This feature will be useful, if only a low number of models has been generated.

### Criteria¶

Typically, models will be **sorted** first according to various (quality/performance) criteria and the best performing models selected by clicking
on the Preselect criteria button.

**Model Criteria**: Models can be sorted according to their training or validation error (\(\text{MSE}_\text{Train}\) or \(\text{MSE}_\text{Valid}\)) or using an average of these two (\(\text{MSE}_\text{TrainValid}\)), either in descending or in ascending order by choosing Best or Worst. From each boot and each start the top \(x\) models (either given by a number or a percentage) can be kept.**Starts/Boots Criteria**: After applying the previous model criteria, from each boot and each random start a certain number or percentage of models is now preselected. Now a selection among the boots and starts can be performed. Each boot/start combination can be characterized and sorted either by the average (Average) or maximum (Max) error of the remaining models. Now a certain number or percentage (to be specified in the Preselection Starts and Preselection Boots fields) of the top-ranked starts (comes first) and boots can be selected.

Note

Any selection based on model and/or starts/boots criteria must be confirmed by pressing the Preselect Criteria button, otherwise these choices have no effect.

Example Assume the following setup: models were built with

10 boots

6 random starts

20 iterations

option keep 8 best models

From the total \(10 \cdot 6 \cdot 20 = 1200\) models, only the best 8 are kept for each boot/start combination, which makes in total \(10 \cdot 6 \cdot 8 = 480\) models appearing in the model selection window. Sorting according to the best \(\text{MSE}_\text{Valid}\) (lowest validation error) and preselecting 25% of those, leaves 2 models for each start/boot combination (those with the lowest validation error), in total 120 models.

Selecting Average in the starts/boot criteria model sorting, calculates the average error of these 2 models for each starts/boot combination,
resulting in \(10 \cdot 6 = 60\) average errors. Choosing 50% as *starts preselection* selects the best 3 (of the 6) starts from each boot. Finally, the
remaining 60 models (10 boots, 3 random starts, 2 models per start) are averaged per boot and choosing e.g. 50% as *boot preselection* only keeps
\(5 \cdot 3 \cdot 2 = 30\) models.

Important

It is recommended to leave the option `Allow Auto-Tune`

checked. In case the user enters percentages don’t fully match with current boots/starts/models setup,
these will be automatically reassigned to a value that makes more sense.

For example, assume learning contains 15 boots and it is manually stopped before starting boot number 2. In this case if `Allow Auto-Tune`

is checked,
`Preselection Boots [%]`

will be set to 100% as only 1 boot was computed – irrespective of the user setting.

Once the **model selection** is **completed**, the report appears in the main bar. Clicking on it leads the user to the *Reports* section.

Switching between different reports is done via the drop-down menu – all reports will be numbered consecutively. The two main actions in the **Report section**
are the generation of model quality reports (upper icon) and the visualization of the model performance via 2D plots (lower icon).