4. For each of the following, answer briefly (no formal math needed explain intuitively). (30 points)...

  

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4. For each of the following, answer briefly (no formal math needed explain intuitively). (30 points) (a) Given a training dataset with N features, is the number of nodes in any decision tree learned from this dataset guaranteed to be lesser than or equal to N? Why or why not? Briefly explain. (10 points) (b) Suppose we have a linearly separable dataset, and we divide the data into training and validation sets. Will a perceptron learned on the training dataset (assuming gradient decent works perfectly well) be guaranteed to have i) 0 error on the training dataset ii) 0 error on the validation dataset. Briefly explain. (10 points) (c) Suppose your boss asks you design a ML algorithm for real-time prediction. Specifically, the requirement is that the ML algorithm needs to preform predic- tions very quickly. Can decision trees be used for such an application? Briefly explain your reasoning. (5 points) 1 (d) Given a dataset where the dataset is not linearly-separable, and each of the fea- tures have continuous values, which of the following algorithms is more ideally suited a) perceptron b) decision-trees c) neural-networks. Why? (5 points) 4. For each of the following, answer briefly (no formal math needed explain intuitively). (30 points) (a) Given a training dataset with N features, is the number of nodes in any decision tree learned from this dataset guaranteed to be lesser than or equal to N? Why or why not? Briefly explain. (10 points) (b) Suppose we have a linearly separable dataset, and we divide the data into training and validation sets. Will a perceptron learned on the training dataset (assuming gradient decent works perfectly well) be guaranteed to have i) 0 error on the training dataset ii) 0 error on the validation dataset. Briefly explain. (10 points) (c) Suppose your boss asks you design a ML algorithm for real-time prediction. Specifically, the requirement is that the ML algorithm needs to preform predic- tions very quickly. Can decision trees be used for such an application? Briefly explain your reasoning. (5 points) 1 (d) Given a dataset where the dataset is not linearly-separable, and each of the fea- tures have continuous values, which of the following algorithms is more ideally suited a) perceptron b) decision-trees c) neural-networks. Why? (5 points)

Answer rating: 100% (QA)

a It is not guaranteed that a decision tree with N nodes learnt from a dataset of N features would have N nodes or less This is due to the fact that w... View the full answer