UNIT-2-ML

What strategies can help reduce overfitting in decision trees?

(i) Enforce a maximum depth for the tree

(ii) Enforce a minimum number of samples in leaf nodes

(iii) Pruning

(iv) Make sure each leaf node is one pure class

• (A) All

• B) (i), (ii) and (iii)

• (C) (i), (iii), (iv)

• (D) None

Which of the following is a widely used and effective machine learning algorithm based on the idea of bagging?

• (A) Decision Tree

• (B) Random Forest

• (C) Regression

• (D) Classification

To find the minimum or the maximum of a function, we set the gradient to zero because which of the following

• (A) Depends on the type of problem

• (B) The value of the gradient at extrema of a function is always zero

• (C) Both (A) and (B)

• (D) None of these

Which of the following is a disadvantage of decision trees?

• (A) Decision trees are prone to be overfit

• (B) Decision trees are robust to outliers

• (C) Factor analysis

• (D) None of the above

What is perceptron?

• (A) A single layer feed-forward neural network with pre-processing

• (B) A neural network that contains feedback

• (C) A double layer auto-associative neural network

• (D) An auto-associative neural network

Which of the following is true for neural networks?

(i) The training time depends on the size of the network.

(ii) Neural networks can be simulated on a conventional computer.

(iii) Artificial neurons are identical in operation to biological ones.

• (A) All

• (B) Only (ii)

• (C) (i) and (ii)

• (D) None

What are the advantages of neural networks over conventional computers?

(i) They have the ability to learn by example.

(ii) They are more fault tolerant.

(iii)They are more suited for real time operation due to their high ‘computational’ rates.

• (A) (i) and (ii)

• (B) (i) and (iii)

• (C) Only (i)

• (D) All

• (E) None

What is Neuro software?

• (A) It is software used by Neurosurgeon

• (B) Designed to aid experts in real world

• (C) It is powerful and easy neural network

• (D) A software used to analyze neurons

Which is true for neural networks?

• (A) Each node computes it’s weighted input

• (B) Node could be in excited state or non-excited state

• (C) It has set of nodes and connections

• (D) All of the above

What is the objective of backpropagation algorithm?

• (A) To develop learning algorithm for multilayer feedforward neural network, so that network can be trained to capture the mapping implicitly

• (B) To develop learning algorithm for multilayer feedforward neural network

• (C) To develop learning algorithm for single layer feedforward neural network

• (D) All of the above