Step-by-step explanation:
First, note that a flexible statistical learning method refers to using models that take into account agree difference in the observed data set, and are thus adjustable. While the inflexible method usually involves a model that has no regard to the kind of data set.
a) The sample size n is extremely large, and the number of predictors p is small. (BETTER)
In this case since the sample size is extremely large a flexible model is a best fit.
b) The number of predictors p is extremely large, and the number of observations n is small. (WORSE)
In such case overfiting the data is more likely because of of the small observations.
c) The relationship between the predictors and response is highly non-linear. (BETTER)
The flexible method would be a better fit.
d) The variance of the error terms, i.e. σ2=Var(ϵ), is extremely high. (WORSE)
In such case, using a flexible model is a best fit for the error terms because it can be adjusted.
Answer:
Ok, so the equation would be 6-2*12+5. The answer to that is -13.
Step-by-Step Explanation:
You would use PEMDAS. First, you multiply 2 and 12 and that's 24. Now your equation is 6-24+5. Then you subtract 6 and 24 and that's -18. Now your equation is -18+5, and the answer to that equation is -13. I hope this helps you!!
Answer:
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Step-by-step explanation:
Answer:
By dividing the top number by the bottom.
Step-by-step explanation:
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