Top 350+ Solved Machine Learning (ML) MCQ Questions Answer
Q. Having multiple perceptrons can actually solve the XOR problem satisfactorily: this is because each perceptron can partition off a linear part of the space itself, and they can then combine their results.
a. true – this works always, and these multiple perceptrons learn to classify even complex problems
b. false – perceptrons are mathematically incapable of solving linearly inseparable functions, no matter what you do
c. true – perceptrons can do this but are unable to learn to do it – they have to be explicitly hand-coded
d. false – just having a single perceptron is enough
Q. Which one of the following is not a major strength of the neural network approach?
a. neural network learning algorithms are guaranteed to converge to an optimal solution
b. neural networks work well with datasets containing noisy data
c. neural networks can be used for both supervised learning and unsupervised clustering
d. neural networks can be used for applications that require a time element to be included in the data
Q. The network that involves backward links from output to the input and hidden layers is called
a. self organizing maps
b. perceptrons
c. recurrent neural network
d. multi layered perceptron
Q. In an election for the head of college, N candidates are competing against each other and people are voting for either of the candidates. Voters don’t communicate with each other while casting their votes.which of the following ensembles method works similar to the discussed elction Procedure?
a. ??bagging
b. boosting
c. stacking
d. randomization
Q. In which neural net architecture, does weight sharing occur?
a. recurrent neural network
b. convolutional neural network
c. . fully connected neural network
d. both a and b
Q. Which of the following are correct statement(s) about stacking?1. A machine learning model is trained on predictions of multiple machine learning models2. A Logistic regression will definitely work better in the second stage as compared to other classification methods3. First stage models are trained on full / partial feature space of training data
a. 1 and 2
b. 2 and 3
c. 1 and 3
d. 1,2 and 3
Q. Given above is a description of a neural network. When does a neural network model become a deep learning model?
a. when you add more hidden layers and increase depth of neural network
b. when there is higher dimensionality of data
c. when the problem is an image recognition problem
d. when there is lower dimensionality of data
Q. What are the steps for using a gradient descent algorithm?1)Calculate error between the actual value and the predicted value2)Reiterate until you find the best weights of network3)Pass an input through the network and get values from output layer4)Initialize random weight and bias5)Go to each neurons which contributes to the error and change its respective values to reduce the error
a. 1, 2, 3, 4, 5
b. 4, 3, 1, 5, 2
c. 3, 2, 1, 5, 4
d. 5, 4, 3, 2, 1
Q. The F-test
a. an omnibus test
b. considers the reduction in error when moving from the complete model to the reduced model
c. considers the reduction in error when moving from the reduced model to the complete model
d. can only be conceptualized as a reduction in error