5. A random forest is an ensemble learning method that attempts to decrease the

Question

5. A random forest is an ensemble learning method that attempts to decrease the bias of decision trees. a) TRUE b) FALSE 6 An infinite depth binary decision tree can always achieve 100% training accuracy, provided that no point is mislabeled in the training set. (a) TRUE (b) FALSE 7. K-means clustering looks to find a low-dimensional representation of the observations that explain a good fraction of the variance. (a) TRUE (b) FALSE 8. In linear SVMs, widening the margin increases the number of observations that violate the margin, thus lowering the variance of the classifier. (a) TRUE (b) FALSE 9. A natural spline is a regression spline with the additional constraints that the function is required to be linear at the boundaries. (a) TRUE (b) FALSE

Explanation / Answer

5. True ,Random forests or random decision forests are an ensemble learning method for classification, regression and other tasks, that operate by constructing a multitude of decision trees

7. False K-Means looks to find homogeneous subgroups among the observations.

8.true.

9.true Natural Splines. A natural spline is a regression spline with additional boundary constraints. The function is required to be linear at the boundary

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