Wednesday, 27 May 2020

Machine Learning Exam Questions TRUE or FALSE 12

Machine learning quiz questions TRUE or FALSE with answers, important machine learning interview questions for data science, Top 3 machine learning question set, ML exam questions


Machine Learning TRUE / FALSE Questions - SET 12


1. The Bayesian Network associated with the following computation of a joint probability P(A) * P(B) * P(C | A, B) * P(D | C) * P(E | B, C) has arcs from node A to C, from B to C, from B to E, from C to D, from C to E, and no other arcs.
(a) TRUE                                                   (b) FALSE

View Answer

Answer: TRUE
The Bayesian network as per the given specification is as follows, if you draw a Bayesian network;

The joint probability of this Bayesian networks = P(A) * P(B) * P(C | A, B) * P(D | C) * P(E | B, C)


2. LASSO is a parametric method.
(a) TRUE                                                   (b) FALSE

View Answer

Answer: TRUE
Least Absolute Shrinkage and Selection Operator (LASSO) is one of the parametric methods. It is a regression analysis method that performs both variable selection and regularization in order to enhance the prediction accuracy and interpretability of the statistical model it produces.
A parametric algorithm has a fixed number of parameters.  A parametric algorithm is computationally faster, but makes stronger assumptions about the data. Most well-known statistical methods are parametric. Other parametric method is Ridge regression.


3. Dimensionality reduction can be used as pre-processing for machine learning algorithms like decision trees, kd-trees, neural networks etc.
(a) TRUE                                                   (b) FALSE

View Answer

Answer: TRUE
Dimensionality reduction is the process of reducing the number of random variables or attributes under consideration. High-dimensionality data reduction, as part of a data pre-processing-step, is extremely important in many real-world applications.
Overfitting is quite common with decision trees simply due to the nature of their training. This could be overcome by performing dimensionality reduction.
When k is large, the k-D tree is inefficient because the splits do not reduce the minimum distance effectively and the search degenerates to exhaustion. Dimensionality reduction can be helpful here.

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