Advanced Database Management System - Tutorials and Notes: Machine Learning Multiple Choice Questions and Answers 10

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Friday, 8 May 2020

Machine Learning Multiple Choice Questions and Answers 10

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 10



1. The numerical output of a sigmoid node in a neural network:

a) Is unbounded, encompassing all real numbers.
b) Is unbounded, encompassing all integers.  
c) Is bounded between 0 and 1.
d) Is bounded between -1 and 1.

View Answer

Answer: (d) all of the above
The function that determines the output of a neuron is known as the activation function. The activation function is a mathematical “gate” in between the input feeding the current neuron and its output going to the next layer.
The Sigmoid function takes a value as input and outputs another value between 0 and 1. It is non-linear and easy to work with when constructing a neural network model. The good part about this function is that continuously differentiable over different values of z and has a fixed output range.

Unlike linear function, the output of the sigmoid activation function is always going to be in range (0,1)


2. What would you do in PCA to get the same projection as SVD?

a) Transform data to zero mean
b) Transform data to zero median
c) Not possible
d) None of these

View Answer

Answer: (a) transform data to zero mean
When the data has a zero mean vector PCA will have same projections as SVD, otherwise you have to centre the data first before taking SVD.

3. Regarding bias and variance, which of the following statements are true? (Here ‘high’ and ‘low’ are relative to the ideal model.)


a) Models which overfit have a high bias.
b) Models which overfit have a low bias.
c) Models which underfit have a high variance.
d) Models which underfit have a low variance

View Answer

Answer: (b) and (d) models which overfit have a low bias and models which underfit have a low variance

Overfitting: Good performance on the training data, poor generliazation to other data.

In supervised learning, overfitting happens when the model captures the noise along with the underlying pattern in data. These models have low bias and high variance

Underfitting: Poor performance on the training data and poor generalization to other data

In supervised learning, underfitting happens when a model is unable to grasp the basis of data pattern. These models usually have high bias and low variance.

4. Given a large dataset of medical records from patients suffering from heart disease, try to learn whether there might be different clusters of such patients for which we might tailor separate treatments. What kind of learning problem is this?

a) Supervised learning
b) Unsupervised learning
c) Both (a) and (b)
d) Neither (a) nor (b)

View Answer

Answer: (b) Unsupervised learning
This is an unsupervised learning problem. In unsupervised learning we feed only the input and let the algorithm to detect the output. Clustering algorithm can be used to solve this problem by grouping patients into different clusters.

5. Predicting on whether will it rain or not tomorrow evening at a particular time is a type of _________ problem.


a) Classification
b) Regression
c) Unsupervised learning
d) All of the above

View Answer

Answer: (c) Classification problem
The result expected here is either yes or no. That means, it can fall into one of these classes. Classification is appropriate when we are trying to predict one of a small number of discrete-valued outputs, such as whether it will rain (which we might designate as class 0), or not (say class 1).


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