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)
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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.
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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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