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3 Machine Learning Quiz Questions with Answers explanation, Interview
questions on machine learning, quiz questions for data scientist answers
explained, machine learning exam questions, question bank in machine
learning, k-nearest neighbor, decision tree, linear regression
Machine
learning Quiz Questions - Set 31
1. Which of the
following machine learning algorithms has both training and test phases?
a) k-Nearest
Neighbor
b) Linear
regression
c) Case-based reasoning
d) None of the
above
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Ans : (b)
|
Answer: (b) Linear regression
Linear regression
is one of the most commonly used predictive modeling techniques. Regression
models are supervised learning models that are generally used when the value
to be predicted is of discrete or quantitative nature. The task of linear regression
model is to obtain a line that best fits the data. In linear regression, we
train the model on training data set to find the linear equation.
|
2. Given a kNN
classifier, which one of the following statements is true?
a) The more
examples are used for classifying an example, the higher accuracy we obtain
b) The more
attributes we use to describe the examples the more difficult is to obtain high
accuracy
c) The most costly part
of this method is to learn the model
d) We can use kNN
for classification only
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Ans : (b)
|
Answer: (b) The more attributes we use to describe the examples
the more difficult is to obtain high accuracy
kNN becomes
significantly slower as the number of examples (independent variables)
increases. When the number of features increases, then it requires more data.
When there’s more data, it creates an overfitting problem because no one
knows which piece of noise will contribute to the model. kNN performs better
with low dimensions (low number of features). For more, you can refer here. https://neptune.ai/blog/knn-algorithm-explanation-opportunities-limitations
|
3. Decision trees
can work with
a) Only numeric values
b) Only nominal
values
c) Both numeric and
nominal values
d) Neither numeric
nor nominal values
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Ans : (c)
|
Answer: (c) Both numeric and nominal values
Decision trees
can handle both numerical and categorical data. Early decision trees were
only capable of handling categorical variables, but more recent versions,
such as C4.5, CART do not have this limitation. The categorical data are
encoded, if required (eg. one-hot encoding), and used by decision tree
algorithms.
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Related links:
When it is difficult to achieve high accuracy in knn?
Multiple choice quiz questions in machine learning
How to use categorical data in decision trees?
List the machine learning algorithms that uses both training and testing
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