1. True or False "True' ' implies the statement is always true, and statements are made in the context of this class. Provide a one sentence explanation for your answer. (a) In random forests, all the bootstrap decision trees are grown without pruning. (b) Random forests can overfit if the number of trees is too large. (c) In boosting, the final model combines the weighted average of all individual models, where the weights are determined by the model's performance during training. (d) The OOB error can be a biased estimate of test error if the number of trees is too small. (e) In bagging, each model is trained on a unique subset of the data, with no overlap between the subsets. (f) A feature with high variable importance in a random forest model will always have a significant effect on the predictions for each individual tree