Friday, 8 May 2020

Machine Learning Multiple Choice Questions and Answers 06

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 06




1. Which of the following guidelines is applicable to initialization of the weight vector in a fully connected neural network.


a) Should not set it to zero since otherwise it will cause overfitting
b) Should not set it to zero since otherwise (stochastic) gradient descent will explore a very small space
c) Should set it to zero since otherwise it causes a bias
d) Should set it to zero in order to preserve symmetry across all neurons

View Answer

Answer: (b) should not set it to zero since otherwise gradient descent will explore a very small space
If we initialize all the weights to zero, the neural network will train but all the neurons will learn the same features during training. Setting all weights to zero makes your model equivalent to a linear model. When you set all weight to 0, the derivative with respect to loss function is the same for every w in weight matrix, thus, all the weights have the same values in the subsequent iteration. Hence, they must be initialized to random numbers.

2. Given two Boolean random variables, A and B, where P(A) = ½, P(B) = 1/3, and P(A | ¬B) = ¼, what is P(A | B)?

a) 1/6
b) ¼
c) ¾
d) 1

View Answer

Answer: (d) 1
P(A | B) = (P(B | A) P(A)) / P(B) = 3/2 P(B | A).
P(B | A) = 1 – P(~B | A), and P(~B | A) = (P(A | ~B) P(~B)) / P(A) = 1/3,
so P(B | A) = 2/3 and therefore
P(A | B) = (2/3)(3/2) = 1

3. For a neural network, which one of these structural assumptions is the one that most affects the trade-off between underfitting (i.e. a high bias model) and overfitting (i.e. a high variance model):


a) The number of hidden nodes
b) The learning rate
c) The initial choice of weights
d) The use of a constant-term unit input

View Answer

Answer: (a) The number of hidden nodes
The number of hidden nodes. 0 will result in a linear model, which many (with non-linear activation) significantly increases the variance of the model. A feed forward neural network without hidden nodes can only find linear decision boundaries.
The Hidden nodes perform computations and transfer information from the input nodes to the output nodes. A collection of hidden nodes forms a “Hidden Layer”. While a feedforward network will only have a single input layer and a single output layer, it can have zero or multiple Hidden Layers.

Overfitting: If there are so many neurons in the hidden layers it might cause Overfitting. Overfitting occurs when unnecessary more neurons are present in the network.

Underfitting: If the number of neurons are less as compared to the complexity of the problem data it takes towards the Underfitting. It occurs when there are few neurons in the hidden layers to detect the signal in complicated data set.


4. You've just finished training a decision tree for spam classification, and it is getting abnormally bad performance on both your training and test sets. You know that your implementation has no bugs, so what could be causing the problem?

a) Your decision trees are too shallow.
b) You need to increase the learning rate.
c) You are overfitting.
d) None of the above.

View Answer

Answer: (a) your decision trees are too shallow
Shallow decision trees - trees that are too shallow might lead to overly simple models that can’t fit the data.
A model that is underfit will have high training and high testing error. Hence, bad performance on training and test sets indicates underfitting which means the set of hypotheses are not complex enough (decision trees that are shallow ) to include the true but unknown prediction function.

The shallower the tree the less variance we have in our predictions; however, at some point we can start to inject too much bias as shallow trees (e.g., stumps) are not able to capture interactions and complex patterns in our data.


5. ___________ refers to a model that can neither model the training data nor generalize to new data.

a) good fitting
b) overfitting
c) underfitting
d) all of the above

View Answer

Answer: (c) underfitting
An underfit machine learning model is not a suitable model and will be obvious as it will have poor performance on the training data. Usually, a model that is underfit will have high training and high testing error

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