I perform matrix factorizaition in my data using the sklearn implementation of Non Negative Matrix Factorization. In the evaluation process I am removing some values from my initial dataset and I am trying to see if the approximation matrix of NMF can predict well those missing values. What I have notice from the experimental results is that, the smaller the latent factors of the decomposition are the better the prediction of the missing value I got. However, this means also that the rest of the values are not approximated really well. Is this an expected behavior? Why this is happening? Moreover, is there any explanation for the l1_ratio and the alpha value?