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59 I used to apply K-fold cross-validation for robust evaluation of my machine learning models. But I'm aware of the existence of the bootstrapping method for this purpose as well. However, I cannot see the main difference between them in terms of performance estimation.
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Even though Roman execution methods did include crucifixion with nails, some scholars believe this method only developed after Jesus’s lifetime. In "cross"-entropy, as the name suggests, we focus on the number of bits required to explain the difference in two different probability distributions. The best case scenario is that both distributions are identical, in which case the least amount of bits are required i.e. simple entropy. I understand cross_validate and how it works, but now I am confused about what cross_val_score actually does.
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Can anyone give me some example? Intuitively cross entropy says the following, if I have a bunch of events and a bunch of probabilities, how likely is that those events happen taking into account those probabilities? If it is likely, then cross-entropy will be small, otherwise, it will be big. In Mathematics Kullback-Leiber divergence (KL), Cross-Entropy (CE), Entropy (H) always mean only one thing, but the term Entropy unfortunately can vary from the scientific community. In any case, the good book on the subject "Information Theory is the book "Elements of Information Theory" by Thomas M. Cover, Joy A.
Thomas." from 1991. 2) So an implementation that uses "cross-entropy" as the loss function, does it use the negative log-likelihood or the cross-entropy w.r.t. the emperical data distribution and the model distribution?