Decision Tree MCQs

 

Decision Tree MCQs

1.     Decision Trees can be used for

A.    Classification Tasks

B.    Regression Tasks

C.    Multi-output tasks

D.    All of the above

Ans: D

 

2.     The iris dataset has

A.    5 features and 3 classes

B.    4 features and 3 classes

C.    2 features and 3 classes

D.    4 features and 2 classes

Ans: B

 

3.     A node’s value attribute tells you how many training instances of each class this node applies to

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  1.  True
  2.  False

Ans: A

4.     A node’s gini attribute measures

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  1.  The number of training instances in the node
  2.  The ratio of training instances in the node
  3.  Its impurity
  4. None of these

Ans: C

5.     If all the training instances of a node belong to the same class then the value of the node's Gini attribute will be

  1.  1
  2.  0
  3.  Any integer between 0 and 1
  4.  A negative value

Ans: B

6.     A Gini coefficient of 1 expresses maximal inequality among the training samples

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A.      True

B.      False

Ans: A

7.     Gini index for a node is found by subtracting the sum of the square of ratio of each classes in a node from 1

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  1.  True
  2.  False

Ans: A

8.     A white box model’s decision are

  1. Usually hard to explain in simple terms
  2. Fairly intuitive and easy to interpret
  3. Both
  4. None

Ans: B

9.     A black box model’s decision are

  1.  Usually hard to explain in simple terms
  2.  Fairly intuitive and easy to interpret
  3. Both
  4. None

Ans: A

10.  Random Forests and Neural networks are examples of

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  1.  White Box Model
  2.  Black Box Model
  3. Both
  4. None

Ans: B

11.  Decision Trees are examples of

  1. White Box Model
  2. Black Box Model
  3. Both
  4. None

Ans: A

12.  A decision tree estimates the probability that an instance belongs to a particular class k by finding the corresponding leaf node for the instance and then returning the ratio of training instances of class k

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  1.  True
  2.  False

Ans: A

13.  The Decision Tree classifier predicts the class which has the highest probability

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  1.  True
  2.  False

Ans: A

14.  If the output of predict_proba is array([[ 0. , 0.90740741, 0.09259259]]) then the predicted class will be

  1. Class 0
  2. Class 1
  3. Class 2
  4. None

Ans: B

15.  The CART algorithm splits the training set in two subsets

  1. Using all the features and a threshold tk
  2. Using a single feature k and a threshold tk
  3. Using half of the features and a threshold k
  4. None

Ans: B

16.  How does the CART algorithm chooses the feature k and the threshold tk for splitting ?

  1.  It randomly chooses a feature k
  2.  It chooses the mean of the values of the feature k as threshold
  3.  It chooses the feature k and threshold tk which produces the purest subsets
  4.  It chooses the feature k and threshold tk such that the gini index value of the subsets is 0

Ans: C

17.  The cost function for finding the value of feature k and threshold tk takes into consideration

  1.  The Gini index values of the subsets
  2.  The number of instances in the subsets
  3.  The total number of instances in the node that is being split
  4.  All of these

Ans: D

18.  Once the CART algorithm has successfully split the training set in two

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  1.  It stops splitting further
  2.  It splits the subsets using the same logic, then the sub- subsets and so on, recursively
  3.  It splits only the right subset
  4.  It splits only the left subset

Ans: B

19.  The CART algorithm stops recursion once it reaches the maximum depth (defined by the max_depth hyperparameter), or if it cannot find a split that will reduce impurity

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  1.  True
  2.  False

Ans: A

20.  Which of the following are correct for the CART algorithm

  1.  It is a greedy algorithm
  2.  It greedily searches for an optimum split at each level
  3.  It does not check whether or not the split will lead to the lowest possible impurity several levels down
  4.  All of the above are correct

Ans: D

21.  While making a prediction in Decision Tree, each node only requires checking the value of one feature

  1.  True
  2.  False

Ans: A

22.  If the total number of training instances is m then the overall complexity of prediction of decision trees is

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  1.  O(m)
  2.  O(mlog(m))
  3.  O(log(m))
  4.  O(m/log(m))

Ans: C

23.  The training algorithm of decision tree compares all features (or less if max_features is set) on all samples at each node

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  1.  True
  2.  False

Ans: A

24.  If the number of features in n and number of training set instances is m then the complexity of training of a decision tree is

  1. O(nmlog(m))
  2. O(mlog(n))
  3. O(nlog(m))
  4. O(mn)

Ans: A

25.  Gini impurity is slightly faster to compute in comparison to entropy

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  •  True
  •  False

Ans: A

26.  Models like Decision Tree models are often called nonparametric model because

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  •  They do not have any parameters
  •  The number of parameters is not determined prior to training
  •  They have lesser parameters as compared to other models
  •  They are easy to interpret and understand

Ans: B

27.  Which of the following is not a regularization parameter for decision tree classifier

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  1.  max_depth
  2.  min_samples_leaf
  3.  max_features
  4.  min_leaf_nodes

Ans: D

28.  Increasing min_* hyperparameters or reducing max_* hyperparameters will regularize the model

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  1.  True
  2.  False

Ans: A

29.  For regression tasks the CART algorithm tries to split the training set in a way that minimizes the MSE

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  1.  True
  2.  False

Ans: A

30.  All the splits made by a Decision Tree are

  1.  Never perpendicular to an axis
  2.  Always perpendicular to an axis
  3.  Always at an acute angle to an axis
  4.  Always at an obtuse angle to an axis

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