Data Analytics | Module 8

Decision tree is the most powerful for ____

• classification

• prediction

• both a and b

• none

Decision tree is a flowchart like_____

• leaf structure

• tree structure

• steam

• none of these

Decision trees can handle_____

• High dimensional data

• low diamesional data

• medium dimensional data

• None

Which of the following is not strength of Decision Tree ?

• able to generate understandable rules

• able to handle both continuous and categorical variables

• less appropriate for estimation tasks

• perform classification without requiring much computation

Which of the following is not weakness of Decision Tree ?

• able to generate understandable rules

• can be computationally expensive to train

• can be computationally expensive to train

• prone to errors in classification problems with many class

Decision-tree algorithm falls under the category of ____

• unsupervised learning algorithms

• reinforcement learning algorithm

• supervised learning algorithms

• prone to errors in classification problems with many class

In Decision-tree algorithm At the beginning, we consider the whole training set as ____

• leaf

• root

• steam

• none

Which of the following is/are true about bagging trees?

  A.  In bagging trees, individual trees are independent of each other

   B. Bagging is the method for improving the performance by aggregating the results of weak learners

• 1

• 2

• 1 and 2

• None

Which of the following is/are true about boosting trees?

    A.In boosting trees, individual weak learners are independent of each other

   B. It is the method for improving the performance by aggregating the results of weak learners

• 1

• 2

• 1 and 2

• none

In Random forest you can generate hundreds of trees (say T1, T2 …..Tn) and then aggregate the results of these tree. Which of the following is true about individual(Tk) tree in Random Forest?

   1 Individual tree is built on a subset of the features

   2 Individual tree is built on all the features

   3 Individual tree is built on a subset of observations

   4 Individual tree is built on full set of observations

• 1 and 3

• 1 and 4

• 2 and 3

• 2 and 4