Comments (1)
Generally, there is no way to do this.
It's possible to implement some differentiation approximation of AUC and optimize over it (e.g. pairwise loss), but for now, it is not implemented.
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Related Issues (20)
- Interaction factors HOT 1
- Custom Loss Function HOT 1
- Question about data format HOT 2
- How to save a trained model? HOT 6
- Use of self.train_w in TFFMCore.init_loss() HOT 2
- Running tffm on a single core HOT 1
- Turning off warnings on tensorflow 1.7
- do you support spars data format HOT 1
- How can I verify the output of a row after loading the model into memory ? HOT 1
- Huge variations in predictions as the seed changes.
- Support for Bayesian Personalized Ranking HOT 1
- is the output of model the same as original FM? HOT 2
- “upper_bound” or “i + batch_size”
- Problem in cross validating the model using sklearn
- Problem: NaN or Inf in w[0]. : Tensor had NaN values HOT 4
- Issue with tensorflow 2.0: module 'tensorflow_core._api.v2.train' has no attribute 'AdamOptimizer' HOT 2
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- ALS / MCME HOT 1
- model.fit(X_tr, y_tr, show_progress=True)
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