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3 Machine Learning Quiz Questions with Answers explanation, Interview
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learning, lazy learner, k-nearest neighbor, eager learner, instance-based learning
Machine
learning Quiz Questions - Set 30
1. Classification
is a type of
a) Reinforcement
learning
b) Supervised learning
c) Unsupervised
learning
d) None of the
above
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Ans : (c)
|
Answer: (c) Supervised learning
What is
classification?
Classification is
the process of finding a model (or function) that describes and distinguishes
data classes or concepts, for the purpose of predicting the class of input
data whose class is unknown.
What is
supervised learning?
Supervised
learning is a type of machine learning algorithm that uses labeled datasets
to train algorithms in order to classify data (or to predict class labels)
accurately.
Why
classification is a supervised learning?
Classification is
a two step process: build the classifier, and use the classifier for
classification. During the building process, a classification algorithm
builds the classifier by analyzing or learning
from a training set which
is made up
of database tuples
and their associated
class labels. As the
class label of
each training tuple
is provided, this step is also known as supervised
learning.
|
2. k-nearest
neighbor (k-NN) is a type of ____ method.
a) Reinforcement
learning
b) Unsupervised
learning
c) Instance-based
learning
d) Lazy learning
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Ans : (c) and (d)
|
Answer: (c) Instance-based learning and (d) lazy learning
Instance-based
learning methods simply store the training examples. Generalizing beyond
these examples is postponed until a new instance must be
classified: given a
new instance, its
relations to the
already stored examples
are examined in order
to assign a
target function value
(the classification) for
the new instance. Due to
this property, instance-based learning
methods are also
called lazy learning
methods.
|
3. Which of the
following is a disadvantage of lazy learning?
a) Difficulty to
maintain
b) Not fit for simultaneous
application to multiple problems
c) Not suitable for
complex and incomplete problem domains
d) Difficult to
handle excessively noise data
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Ans : (d)
|
Answer: (d) Difficult to handle excessively noise data
Excessively noisy
data may result in storing same problems numerous times because of the
differences in cases due to noise. In turn, this implies high memory /
storage requirements and time-consuming search for similar examples.
The other three
options are advantages of lazy learning algorithms.
Easy of maintenance
Suitable for simultaneous
application to multiple problems
Suitable for
complex and incomplete problem domains
|
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Related links:
What is eager learner?
Multiple choice quiz questions in machine learning
What is supervised learning in machine learning
What is instance-based learning?
Which of the classification algorithm is a lazy learner
Why classification is a supervised learning?
What is the difference between instance-based learning and lazy learning?
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