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Suppose the random sample used for each tree in the previous random forest is decreased from 1000 rows to 500 rows. Circle ALL that are true for the ensemble classifier.
a) Variance of predictions are expected to decrease
b) Bias of predictions are expected to decrease
c) Each base classifier is expected to have more out of bag rows of data
d) Generalization error is expected to decrease
Over fitting Considerations : Hence in left unchecked there backpropagation in multi-layer networks can be highly susceptible to overfitting itself to the training examples. B
If quicksort is so quick, why bother with anything else? If bubble sort is so bad, why even mention it? For that matter, why are there so many sorting algorithms?
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We will be using data from the 2008 Canadian General Election Study.1 The data can be found on the course page. As well, there are three other documents (loosely called "codebooks"
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