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1 vote

When do you know training a model is not feasible?

the short answer is that if a human can do it, then the right implementation and model can do it (see "human level performance" or "human benchmarking"). but that doesn't answer if ...
Phillip Maire's user avatar
0 votes

How a Random forest "learns" or How loss (objective function value) is propagated back so that a random forest can "Improve"?

If you understand decision trees and the intuition behind the decision tree then the random forest is simple. To understand the decision tree, we can intuitively consider what we want it to do. We ...
timmy1691's user avatar
2 votes
Accepted

Does ANN returns the same prediction for the same input?

The training (i.e. model.fit) of your neural network is NOT deterministic → every time you train the network, the result of the training (i.e. the model) is ...
noe's user avatar
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