I am trying to learn how to use TFDF. I keep hearing that decision trees based models are fast to train and query so I wanted to see how much I could use them at work.
I have a multilabel classification task so I used StringLookup to map the labels into a multihot vector. The GradientBoostedTreesModel is complaining
ValueError: Can not squeeze dim, expected a dimension of 1, got 1184. I have 1184 unique labels so the transform is fine but apparently that’s not an acceptable format for the target. Is this out of scope for these models? Is it just that I need to pass some parameter to the model object when I create it? I think I saw that multiclass is handled out of the box so do I “simply” need to split the vector into one tensor for each class? That feels a bit inefficient.
Edit: PS is there a better place/forum to ask this question?