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 09
1. The number of
test examples needed to get statistically significant results should be
_________
a) Larger if the
error rate is larger.
b) Larger if the error rate is smaller.
c) Smaller if the
error rate is smaller.
d) It does not
matter.
View Answer
Answer: (b) Larger if the error rate is smaller
Tests for
statistical significance tell us what the probability is that the
relationship we think we have found is due only to random chance. They tell
us what the probability is that we would be making an error if we assume that
we have found that a relationship exists.
Statistical
significance is a way of mathematically proving that a certain statistic is
reliable. When you make decisions based on the results of experiments that
you’re running, you will want to make sure that a relationship actually
exists.
Your statistical
significance level reflects your risk tolerance and confidence level. For
example, if you run an A/B testing experiment with a significance level of
95%, this means that if you determine a winner, you can be 95% confident that
the observed results are real and not an error caused by randomness. It also
means that there is a 5% chance that you could be wrong.
[Source: Statistical Significance]
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2. Neural
networks:
a) Optimize a
convex objective function
b) Can only be
trained with stochastic gradient descent
c) Can use a mix of different activation functions
d) None of the
above
View Answer
Answer: (c) Can use a mix of different activation functions
Neural networks
can use a mix of different activation functions like sigmoid, tanh, and ReLu
functions.
Activation
function
In a neural
network, numeric data points, called inputs, are fed into the neurons in the
input layer. Each neuron has a weight, and multiplying the input number with
the weight gives the output of the neuron, which is transferred to the next
layer. The activation function is a mathematical “gate” in between the input
feeding the current neuron and its output going to the next layer. It can be
as simple as a step function that turns the neuron output on and off,
depending on a rule or threshold. Or it can be a transformation that maps the
input signals into output signals that are needed for the neural network to
function.
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3. Consider the Bayesian network given below. How many independent parameters are needed for this Bayesian Network?
a) 2
b) 4
c) 8
d) 16
View Answer
Answer: (c) 8
Given Bayesian
network model needs 8 independent parameters.
P(H) = 1
P(W) = 1
P(P|W) = 2
P(U|H, P) = 4
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4. For Kernel Regression, which one of these structural assumptions is the one that most affects the trade-off between underfitting and overfitting:
a) Whether kernel
function is Gaussian versus triangular versus box-shaped
b) Whether we use
Euclidian versus L1 versus L∞ metrics
c) The kernel width
d) The maximum
height of the kernel function
View Answer
Answer: (c) The kernel width
Small kernel
width means only training points very close to a test point will influence
the prediction for that test point. This can result in overfitting. On the
other hand, if kernel width is too large, this can result in underfitting.
Kernel regressionKernel regressions are weighted average estimators that use kernel functions as weights. It is a non-parametric technique in statistics to estimate the conditional expectation of a random variable. The objective is to find a non-linear relation between a pair of random variables X and Y. [Wikipedia] |
5. Which one of the following is the main reason for pruning a Decision Tree?
a) To save
computing time during testing
b) To save space
for storing the Decision Tree
c) To make the
training set error smaller
d) To avoid overfitting the training set
View Answer
Answer: (d) to avoid overfitting the training set
The reason for
pruning is that the trees prepared by the base algorithm can be prone to
overfitting as they become incredibly large and complex.
Pruning is a
technique in machine learning and search algorithms that reduces the size of
decision trees by removing sections of the tree that provide little power to
classify instances. Pruning reduces the complexity of the final classifier,
and hence improves predictive accuracy by the reduction of overfitting.
[Wikipedia]
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