Unlike linear regression, neural networks can handle nonlinearities and interaction effects even when these are not explicit input variables.
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Q1: Axons, dendrites, and synapses are studied by
Q2: Neural networks may be represented by an
Q4: It is impossible to assess the relative
Q5: Cross-validation of a neural network (or other)
Q6: What is UNTRUE of hidden layers of
Q7: Explain "gradient descent" and what it does
Q8: What makes a neural network model "supervised"?
A)
Q9: Which is possible with the "caret" package?
A)
Q10: Which package supports the Olden method of
Q11: What was a "violin plot" used for
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