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In CP_ALS and Tucker_ALS, why do you calculate the RMSE on the training set ( sparse_tensor) not on the test set as you do in BGCP?
Btw, final_mape = np.sum(np.abs(dense_tensor[pos] - tensor_hat[pos]) / dense_tensor[pos]) / dense_tensor[pos].shape[0], the np.abs() should cover (dense_tensor[pos] - tensor_hat[pos]) / dense_tensor[pos] instead of dense_tensor[pos] - tensor_hat[pos]
The text was updated successfully, but these errors were encountered:
This is a good question. Of course, you could replace that performance metric, and I will do that soon.
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In CP_ALS and Tucker_ALS, why do you calculate the RMSE on the training set ( sparse_tensor) not on the test set as you do in BGCP?
Btw, final_mape = np.sum(np.abs(dense_tensor[pos] - tensor_hat[pos]) / dense_tensor[pos]) / dense_tensor[pos].shape[0], the np.abs() should cover (dense_tensor[pos] - tensor_hat[pos]) / dense_tensor[pos] instead of dense_tensor[pos] - tensor_hat[pos]
The text was updated successfully, but these errors were encountered: