[Predictive Intelligence] Training ML Solutions [Classification/Similarity/Regression] can take a long time > 12 hours to train and it looks like the training is stuck when using the default Paragraph Vector [PV] settingDetailsIn Predictive Intelligence when training ML Solutions [Classification/Similarity/Regression] using the default Paragraph Vector [PV] setting, for solution definitions with multiple Input fields and a large training dataset [300k+ records], it can take considerable time to complete the ML Solution training and users have reported that the training looks like it is "stuck", because the training has not progressed for 12+ hours. You can also train the same ML Solutions [Classification/Similarity/Regression] using the Term Frequency–Inverse Document Frequency [TF-IDF] setting, which has been shown to reduce the training time considerably with ML Solutions that take a long time to train using the default setting. We have seen ML Solutions that take 15 hours to train using the default Paragraph Vector [PV] setting that are reduced to 1.5 hours training time, when using the Term Frequency–Inverse Document Frequency [TF-IDF] setting. Training a solution with the Term Frequency–Inverse Document Frequency [TF-IDF] setting will generate a much larger trained solution model, which can be 10x larger than a trained solution model using the default Paragraph Vector [PV] setting. However, the model size will not impact the performance on the predictions made when using either trained solutions.Additional InformationFor further information on how to to configure the Term Frequency–Inverse Document Frequency [TF-IDF] setting in your ML Solutions, please review our documentation on Configure TF-IDF for classification, similarity, and regression solutions.