Hello Mathieu,
Thanks for your answer!
The results are hugely different I’d say. I have 3 classes that I’d like to classify, please see the results below!

The result from Sklearn

            precision recall f1-score support
      -1       0.67      0.04      0.07       338
       0       0.71      0.99      0.83      1002
       1       0.00      0.00      0.00        86


The result from TFDF

          precision recall f1-score support
     -1       1.00      0.94      0.97       338
      0       0.90      1.00      0.95      1002
      1       0.00      0.00      0.00        86

I set everything just like the given code snippet. It’s intriguing, isn’t it?
The datasets I used for the 2 models were basically the same - all categorical data (text) was removed - The targets (ground truth) were mapped to positive integer index [0, 1, 2]. Basically, the ingredients for sklearn and TFDF are the same.
Notice that the dataset is very imbalanced, but the TFDF did a very impressive job. This is every cool but I don’t want be fooled by the metrics. I just wanna make sure the models work correctly. ^^