Multiple Classification Model

Multiple Classification uses blocks of text to predict a classification of the text

Select a DSD to use for training. Only those with a purpose of “Machine Learning” will be shown.

Select the Data Group within the DSD that contains the training data.

Two further entries are required:

• Classification Text – the field that represents the blocks of text to be analysed
• Classification – the field that represents the classification for each text

If the model has been trained before, the right hand side of the page shows the metrics of that training. See below for more information on metrics.

Save the training settings and the click on “Train”

For Multiple Classification training, there are no settings.

Click Start to begin the training.

On completion metrics for the training will be shown.

• Log Loss – quantifies the accuracy of the model – the closer to one the better
• LogLossReduction – measure of improvement over random – the closer to one the better
• MacroAccuracy – the average accuracy by class – the closer to one the better
• MicroAccuracy – the fraction of instances predicted correctly – the closer to one the better
• PositivePrecision – proportion of correct positive predictions– the closer to one the better
• TopKAccuracy –a measure of the accuracy of the model - the closer to one the better

Enter a sample block of Classification Text to receive a predicted Classification.