Assignment - 3
Understanding Bayesian Classifiers
Test your knowledge on Bayesian classifiers and their role in machine learning with this engaging quiz! Answer questions ranging from classification tasks to different learning methods, and gain deeper insights into the fundamentals of data analysis.
Key Topics Covered:
- Classification Techniques
- Naive Bayes Algorithm
- Learning Methods: Supervised vs Unsupervised
- Practical Applications in Telecommunications and Earthquake Prediction
Bayesian classifiers is
A class of learning algorithm that tries to find an optimum classification of a set of examples using the probabilistic theory.
Any mechanism employed by a learning system to constrain the search space of a hypothesis
An approach to the design of learning algorithms that is inspired by the fact that when people encounter new situations, they often explain them by reference to familiar experiences, adapting the explanations to fit the new situation.
None of these
Classification is
A measure of the accuracy, of the classification of a concept that is given by a certain theory
The task of assigning a classification to a set of examples
A subdivision of a set of examples into a number of classes
None of these
What is the use of Naive Bayes Algorithm?
Generate mining models
Estimating the probability of a class value during classification and prediction
To make decisions for reporting.
Both a and b
Classification task referred to
A subdivision of a set of examples into a number of classes
The task of assigning a classification to a set of examples
A measure of the accuracy, of the classification of a concept that is given by a certain theory
None of these
The problem of finding hidden structure in unlabeled data is called...
Unsupervised learning
Reinforcement learning
Supervised learning
Data Learning
Task of inferring a model from labeled training data is called
Reinforcement learning
Unsupervised learning
Supervised learning
None of these
Some telecommunication company wants to segment their customers into distinct groups in order to send appropriate subscription offers, this is an example of ...
Supervised learning
Seriation
Unsupervised learning
Data extraction
You are given data about seismic activity in Japan, and you want to predict a magnitude of the next earthquake, this is in an example of ...
Unsupervised learning
Dimensionality reduction
Seriation
Supervised learning
Algorithm is
It uses machine-learning techniques. Here program can learn from past experience and adapt themselves to new situations
Computational procedure that takes some value as input and produces some value as output
Science of making machines performs tasks that would require intelligence when performed by humans
None of these
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