Top 5 Machine Learning Quiz Questions with Answers explanation, Interview questions on machine learning, quiz questions for data scientist answers explained, machine learning exam questions
Machine learning MCQ - Set 01
1. High entropy means that the partitions in classification are
a) pure
b) not pure
c) useful
d) useless
View Answer
Answer: (b) Not pure
Entropy is a measure of the randomness in the
information being processed. The higher the entropy, the harder it is to draw
any conclusions from that information.
It is a measure
of disorder or purity or unpredictability or uncertainty.
Low entropy means
less uncertain and high entropy means more uncertain.
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2. A machine
learning problem involves four attributes plus a class. The attributes have 3,
2, 2, and 2 possible values each. The class has 3 possible values. How many maximum
possible different examples are there?
a) 12
b) 24
c) 48
d) 72
View Answer
Answer: (d) 72
Maximum possible different examples are the products
of the possible values of each attribute and the number of classes;
3 * 2 * 2 * 2 * 3
= 72
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3. Which of the
following is NOT supervised
learning?
a) PCA
b) Decision Tree
c) Linear
Regression
d) Naive Bayesian
View Answer
Answer: (a) PCA
Principal Component Analysis (PCA) is not predictive analysis tool. It is a data pre-processing tool. It helps in picking out the most relevant linear combination of variables and use them in our predictive model.
PCA is a technique for reducing the dimensionality of
large datasets, increasing interpretability but at the same time minimizing
information loss.
Supervised learning
Supervised learning is the machine learning task of learning
a function that maps an input to an output based on example input-output
pairs. It infers a function from labeled training data consisting of a set of
training examples.
Unsupervised learning
Unsupervised learning is a type of machine learning
task where you only have to insert the input data (X) and no corresponding
output variables are needed (or not known). It does not have labeled data for
training. Algorithms are left to their own devices to help discover and
present the interesting structure that is present in the data.
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4. Which of the following statements about Naive Bayes is incorrect?
a) Attributes are
equally important.
b) Attributes are statistically dependent of one another given
the class value.
c) Attributes are
statistically independent of one another given the class value.
d) Attributes can
be nominal or numeric
View Answer
Answer: (b) Attributes are statistically dependent
of one another given the class value
Attributes are
statistically independent of one another given the class value.
Naïve Bayes
Naïve Bayes classifier
assumes conditional independence between attributes and assigns the MAP class
to new instances.
Naive Bayes is a
classification algorithm for binary (two-class) and multi-class
classification problems. The technique is easiest to understand when
described using binary or categorical input values.
It is called naive Bayes because the calculation
of the probabilities for each hypothesis are simplified to make their
calculation tractable. Rather than attempting to calculate the values of each
attribute value P(d1, d2, d3|h), they are assumed to be conditionally independent given the target value and
calculated as P(d1|h) * P(d2|H) and so on.
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5. Suppose we would
like to perform clustering on spatial data such as the geometrical locations of
houses. We wish to produce clusters of many different sizes and shapes. Which
of the following methods is the most appropriate?
a) Decision Trees
b) Density-based clustering
c) Model-based
clustering
d) K-means
clustering
View Answer
Answer: (b) Density-based clustering
The density-based
clustering methods recognize clusters based on density function distribution
of the data object. For clusters with arbitrary shapes, these algorithms
connect regions with sufficiently high densities into clusters.
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