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WorksheetsmachineLearning Test
Total questions: 20
Worksheet time: 10mins
A computer program is said to learn from __________E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.
Training
Experience
Database
Algorithm
What takes input as an object described by a set of attributes?
Tree
Graph
Decision graph
Decision tree
Which of the following does not include different learning methods?
Memorization
Analogy
Introduction
Deduction
Which of the following is the model used for learning?
Decision trees
Neural networks
Propositional and FOL rules
All of the mentioned
In which of the following learning the teacher returns reward and punishment to learner?
Active learning
Reinforcement learning
Supervised learning
Unsupervised learning
Decision trees are appropriate for the problems where
Attributes are both numeric and nominal
Target function takes on a discrete number of values
Data may have errors
All of the mentioned
Full form of MDL.
Minimum Domain Length
Maximum Description Length
Minimum Description Length
None of these
The benefit of Naive Bayes:-
Naive Bayes is one of the fast and easy ML algorithms to predict a class of data sets.
It is the most popular choice for text classification problems
It can be used for Binary as well as Multi-class Classifications
All of the above
Examples of Naive Bayes Algorithm is/are
Spam filtration
Sentimental analysis
Classifying articles
All of the above
Which of the following is correct about the Naive Bayes?
Assumes that all the features in a dataset are independent
Assumes that all the features in a dataset are equally important
Both
All of the above
Which of the following provided by the Bayesian Network?
Complete description of the problem
Complete description of the domain
Partial description of the domain
All of the above
terms are required for building a bayes model
1
2
3
4
Applications of NN (Neural Network)
Risk management
Data validation
Sales forecasting
All of the above
The general tasks that are performed with backpropagation algorithm
Pattern mapping
Prediction
Function approximation
All of the above
Neural Networks are complex _____ functions with any parameters
a) Linear
Non linear
Discreate
Exponential
The backpropagation law is also known as generalized delta rule
True
False
What is the objective of backpropagation algorithm?
To develop learning algorithm for multilayer feedforward neural network, so that network can be trained to capture the mapping implicitly
To develop learning algorithm for multilayer feedforward neural network
To develop learning algorithm for single layer feedforward neural network
All of the above
What are the advantages of neural networks over conventional computers?
(i) They have the ability to learn by example.
(ii) They are more fault tolerant.
(iii)They are more suited for real time operation due to their high ‘computational’ rates.
(i) and (ii)
(i) and (iii)
Only (i)
All
Which of the following is true for neural networks?
(i) The training time depends on the size of the network.
(ii) Neural networks can be simulated on a conventional computer.
(iii) Artificial neurons are identical in operation to biological ones.
All
Only (ii)
(i) and (ii)
None
Neural network learning methods provide a robust approach to approximating
a) Real valued
Discrete-valued
Vector valued
All of the above
