Machine learning (M.L) MCQ's




Question 31 :
What are two steps of pruning in decision tree ?

  1. Pessimistic pruning and Optimistic pruning
  2. Postpruning and Prepruning
  3. Cost complexity pruning and time complexity pruning
  4. High pruning and low pruning
  

Question 32 :
Principal component analysis is a technique for performing

  1. Dimensionality reduction
  2. Pruning
  3. Aggregation
  4. Sampling
  

Question 33 :
Choose the correct tree based learner.

  1. Rule based
  2. Hidden markov model
  3. Bayesian classifier
  4. CART
  

Question 34 :
Consider a point that is correctly classified and distant from the decision boundary. Which of the following methods will be unaffected by this point?

  1. Nearest neighbor
  2. Support Vector Machine
  3. Logistic regression
  4. Linear regression
  

Question 35 :
Choose the reason for pruning a Decision Tree?

  1. To save computing time during testing
  2. To avoid overfitting the training set
  3. To save space for storing the Decision Tree
  4. To make the training set error smaller
  

Question 36 :
In Logistic regression predicted value of Y lies within _____ range.

  1. 0 to 1
  2. 0 to -∞
  3. -∞ to +∞
  4. -1 to 1
  

Question 37 :
In principal component analysis ,if eigenvalues are equal.What does it mean?

  1. PCA will perform outstandingly
  2. PCA will perform badly
  3. No effect
  4. Model will be unstable
  

Question 38 :
Which statement is true about regression problems?

  1. Output attribute must be only categorical.
  2. Output attribute must be only numerical
  3. Output attribute can be either categorical or numerical.
  4. Output attribute can be neither categorical nor numerical.
  

Question 39 :
Multiple regression model has

  1. Only one independent variable
  2. More than one dependent variables
  3. More than one independent variables
  4. Only one dependent variable
  

Question 40 :
Which of the following is not example of Derivative free optimization

  1. Random Search Method
  2. Downhill simplex method
  3. Genetic algorithm
  4. Steepest Descent
  

Question 41 :
Support Vector Machine(SVM) can be used for both classification or regression challenges.Which kind of learning technique SVM uses?

  1. supervised
  2. unsupervised
  3. reinforced
  4. clustered
  

Question 42 :
Given a large dataset of medical records from patients suffering from heart disease, try to learn whether there might be different clusters of such patients for which we might tailor separate treatments. What kind of learning problem is this?

  1. Supervised learning
  2. Unsupervised learning
  3. Reinforcement learning
  4. Semi-Supervised Learning
  

Question 43 :
The average positive difference between computed and desired outcome values.

  1. root mean squared error
  2. mean squared error
  3. mean absolute error
  4. mean positive error
  

Question 44 :
You are given a labeled binary classification data set with N data points and D features. Suppose that N < D. In training an SVM on this data set, which of the following kernels is likely to be most appropriate?

  1. Linear kernel
  2. Quadratic kernel
  3. Higher-order polynomial kernel
  4. RBF kernel
  

Question 45 :
Let us implement a single neuron with threshold activation function to simulate working of logical AND gate.Give the correct values of weights and threshold.

  1. w1=1,w2=-1,T=-1
  2. w1=-1,w2=-1,T=-1
  3. w1=1,w2=1,T=2
  4. w1=-1,w2=1,T=-2
  

Question 46 :
Which of the following is not a clustering algorithm?

  1. EM-Algorithm
  2. K-means clustering
  3. Radial Basis Function
  4. Decision Tree
  

Question 47 :
You ran gardient descent for 20 iterations with learning rate=0.2 and compute cost for each iteration.You observe that cost decreases after each iteration .Based on this which conclusion is more suitable.

  1. Try smaller values of learning rate like 0.01
  2. 0.2 is effective choice of learning rate.
  3. Try larger values of learning rate like 1.
  4. Try any negetive value for learning rate
  

Question 48 :
What is the approach of basic algorithm for decision tree induction?

  1. Greedy
  2. Top Down
  3. Procedural
  4. Step by Step
  

Question 49 :
Negative sign of weight indicates?

  1. excitatory input
  2. inhibitory input
  3. excitatory output
  4. inhibitory output
  

Question 50 :
The amount of output of one unit received by another unit depends on what?

  1. output unit
  2. input unit
  3. activation value
  4. weight
  
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