Which of the following are true statements? (Select All correct answers)A. Decision Trees and Neural Nets can both handle very complex modeling tasks with and can produce high-variance overfit models.B. Decisions Trees are easily interpreted, but Neural Nets are hard to interpretC. If your Decision Tree is overfit, you should increase the size of the tree, and if your Neural net is overfit, you should increase the number of hidden layers.

Question 3What is overfitting in machine learning?a) Overfitting occurs when a model has high complexity and captures both information and noise in the training data.b) Overfitting occurs when a model has poor performance on the training data.c) Overfitting is indicated when a model has good performance on the training dataset but relatively poor performance on the testing dataset.d) Overfitting occurs when a model has good performance on the test data.Answer choicesSelect only one optionREVISITa onlya & da & ca, c & d

Which of the following statements about bias and variance are true? (Select TWO correct answers) A. High bias models are typically underfit. B. Overfitting tends to lead to models with high variance and low bias. C. You can usually optimize both bias and variance simultaneously by choosing a more complex model. D. You can usually optimize both bias and variance simultaneously by choosing better hardware with GPUs.

What are the disadvantages of the decision tree?*1 point(A) Over-fitting of the data is possible.(C) We have to balance the dataset before training the model(B) The small variation in the input data can result in a different decision tree(D) All of the above

Which of the following statements is not true about the Decision tree?1 pointa) It starts with a tree with a single leaf and assign this leaf a label according to a majority vote among all labels over the training setb) It performs a series of iterations and on each iteration, it examine the effect of splitting a single leafc) It defines some gain measure that quantifies the improvement due to the splitd) Among all possible splits, it either choose the one that minimizes the gain and perform it, or choose not to split the leaf at all

Which of the following is a disadvantage of decision trees?Review LaterThey are computationally expensive to trainThey are prone to overfittingThey require labeled data for trainingThey are not suitable for high-dimensional data