In this view you will be able to modify the generic settings of a model. There are two sections: the first one is related to the model’s general settings, and the second one enables to modify the classification settings.

Model settings

Model settings

  • Model name: the name of the model is how it will be listed in the resources section and how it will be called to classify any text. It is limited to 64 characters, and can contain only alphanumeric characters, dashes and underscores. You can change it at any time.
  • Language: this is the language configured for this model. It only applies for the stopwords list, configuring it according to the language selected.


    If you modify the language of the model and you added any stopwords to your list, the complete list will be removed and replaced with the default one for the new language, so all your modifications and additions will be lost. Mind this before saving!

  • Description: a brief description of the model. It is limited to 1024 characters.

Classification settings

  • Minimum absolute relevance: it establishes the minimum absolute relevance for a category to be accepted as a valid result. In other words, it filters the results you'll see in the response by the relevance value obtained in the classifier. The value assigned by default is 0.06, but depending on the model type you are working with, the absolute relevance value range of the results will change, so you can adjust this parameter to optimize the results after the evaluation.
  • Minimum relative relevance: it establishes the minimum relative relevance of a category to be accepted as a valid result. In other words, it filters the results you'll see in the response if its relevance value with respect to the more relevant category is below the configured threshold.

    The default value is 0. See an example of the filtering in the table below, according to different values of the parameter:

    Category Absolute relevance Relative relevance Threshold = 0.3 Threshold = 0.51
    Category 1 4 = 4/4 = 1
    Category 2 2 = 2/4 = 0.5
    Category 3 1 = 1/4 = 0.25
  • Term boost: it controls the balance between the effect of the statistical classifier based on the training text and the terms in rules. Allowed values range from 0.0 to infinite; higher values give more importance to rules whereas lower values make the training text more important. The default value is 1.0.


    Cases in which rules are very important to obtain the correct order in the classification, you may increase the value of the term boost parameter (5 to 10 are usual values for these scenarios).