Machine Learning Algorithms Exam Questions with
Answers
1. Machine learning algorithms can be divided into 3 broad categories
correct answer: supervised learning, unsupervised learning, and reinforcement
learning.Supervised learning is useful in cases where a property (label) is available for a certain
dataset (training set), but is missing and needs to be predicted for other instances. Unsupervised
learning is useful in cases where the challenge is to discover implicit relationships in a given unlabeled
dataset (items are not pre-assigned). Reinforcement learning falls between these 2 extremes — there
is some form of feedback available for each predictive step or action, but no precise label or error
message.
2. Decision Trees correct answer: Supervised. A decision tree is a decision support tool that uses a
tree-like graph or model of decisions and their possible consequences, including chance-event
outcomes, resource costs, and utility. Take a look at the image to get a sense of how it looks like.
From a business decision point of view, a decision tree is the minimum number of yes/no questions that
one has to ask, to assess the probability of making a correct decision, most of the time. As a method, it
allows you to approach the problem in a structured and systematic way to arrive at a logical conclusion.
3. Naïve Bayes Classification correct answer: Supervised. Naïve Bayes classifiers are a family
of simple probabilistic
classifiers based on applying Bayes' theorem with strong (naïve) independence assumptions between
the features. The featured image is the equation — with P(A|B) is posterior probability, P(B|A) is
likelihood, P(A) is class prior probability, and P(B) is predictor prior probability.
Some of real world examples are correct answer:
To mark an email as spam or not spam
Classify a news article about technology, politics, or sports
Check a piece of text expressing positive emotions, or negative
emotions? Used for face recognition software.
4. Ordinary Least Squares Regression correct answer: Supervised. Least squares is a
method for performing linear
1/
7
, regression. You can think of linear regression as the task of fitting a straight line through a set of points.
There are multiple possible strategies to do this, and "ordinary least squares" strategy go like this —
You can draw a line, and then for each of the data points, measure the vertical distance between the
point and the line, and add these up; the fitted line would be the one where this sum of distances is as
small as possible.
Linear refers the kind of model you are using to fit the data, while least squares refers to the kind of
error metric you are minimizing over.
2/
7
Answers
1. Machine learning algorithms can be divided into 3 broad categories
correct answer: supervised learning, unsupervised learning, and reinforcement
learning.Supervised learning is useful in cases where a property (label) is available for a certain
dataset (training set), but is missing and needs to be predicted for other instances. Unsupervised
learning is useful in cases where the challenge is to discover implicit relationships in a given unlabeled
dataset (items are not pre-assigned). Reinforcement learning falls between these 2 extremes — there
is some form of feedback available for each predictive step or action, but no precise label or error
message.
2. Decision Trees correct answer: Supervised. A decision tree is a decision support tool that uses a
tree-like graph or model of decisions and their possible consequences, including chance-event
outcomes, resource costs, and utility. Take a look at the image to get a sense of how it looks like.
From a business decision point of view, a decision tree is the minimum number of yes/no questions that
one has to ask, to assess the probability of making a correct decision, most of the time. As a method, it
allows you to approach the problem in a structured and systematic way to arrive at a logical conclusion.
3. Naïve Bayes Classification correct answer: Supervised. Naïve Bayes classifiers are a family
of simple probabilistic
classifiers based on applying Bayes' theorem with strong (naïve) independence assumptions between
the features. The featured image is the equation — with P(A|B) is posterior probability, P(B|A) is
likelihood, P(A) is class prior probability, and P(B) is predictor prior probability.
Some of real world examples are correct answer:
To mark an email as spam or not spam
Classify a news article about technology, politics, or sports
Check a piece of text expressing positive emotions, or negative
emotions? Used for face recognition software.
4. Ordinary Least Squares Regression correct answer: Supervised. Least squares is a
method for performing linear
1/
7
, regression. You can think of linear regression as the task of fitting a straight line through a set of points.
There are multiple possible strategies to do this, and "ordinary least squares" strategy go like this —
You can draw a line, and then for each of the data points, measure the vertical distance between the
point and the line, and add these up; the fitted line would be the one where this sum of distances is as
small as possible.
Linear refers the kind of model you are using to fit the data, while least squares refers to the kind of
error metric you are minimizing over.
2/
7
