After a successful product annotation you are able to create and train a machine learning model. This model determines all annotations for all products by itself based on user-annotated products. A part of these annotations is the training set. Later the full user annotations are compared with the model annotations to get the model prediction accuracy. Or more More precisely: , the more the model predicts correctly the higher is the accuracy.
Table of Contents:
...
To create a model you need at least one product annotation for the new attribute. Note that for a good prediction we need at least round about round about 10 percent of products to be annotated.
In the Annotation view on the right-hand side you see a text that says, 'currently no model exist exists' and a button to 'create a model.'
Set Model Properties
Clicking on the "Create Model" button opens a modal window. In this window you have to set the training attributes for this model.
| Panel | ||||
|---|---|---|---|---|
| ||||
Basically, the following scenario applies: Example: |
...
- Name:
The model name; initially set as a combination of attribute name + the suffix "Model" - Last Modified:
Date were indicating when the model was created or changed (retrained) the last time. - Status:
Two states are possible:- Active: Model was applied to the data feed.
- Inactive: Model was not applied, deactivated or invalid/incompatible (see model states below).
- Performance:
Contains some statistics of the model - Trained Attribute Set:
This list contains all attributes which were used to train the current model. - Retrain Model:
Opens the same modal window like after clicking the "Create Model" button. You can either use the same set of training attributes again or choose a different set. - Delete:
Removes the model irreversiblepermanently. All predicted annotations get lost. - Apply to Data:
Applies the created model to all products for the new attribute. Afterwards, all product products have a product annotation.
...
If you are not happy with the prediction results of the model, it is possible to retrain it to get better results. Furthermore, for some model states (invalid and incompatible) a retraining is mandatory to use the attribute in your Product Guide.
The structure of the modal window is exactly the same as for the model creation. The only difference is that the selected attributes are the last used training attributes. After adapting your changes and clicking the "Start" button, the model will be retrained. Afterwards you can look at the performance stats and value predictions to see if the adaptations was were worth it.
| Note | ||
|---|---|---|
| ||
You cannot revert the model to an older state. That means after the retraining the old model status is gone for good. |
Model States
...
You Your model can reach different state levels during its existence. Each level is worse than the upper one.
Furthermore, a state can only get worse for one or multiple levels, but not vice versa. The only level where it can reach upwards again is for the state 'valid.'
1) Valid
The annotation model is conformal with the annotated values and product data.
This state should always be the aim.
2) Outdated
The model is still valid, but there are new annotation values which could change the model predictions.
You should consider to retrain retraining the model with the new information.
...
In this case a model retraining is mandatory. Otherwise, the attribute cannot be applied to the data respectively used in the Concept Board.
...