Cs- 1750 Ml SEC- A Quiz 2
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Questions and Answers
- 1.
Where does the bayes rule can be used?
- A.
Solving queries
- B.
Increasing complexity
- C.
Decreasing complexity
- D.
Answering probabilistic query
Correct Answer
D. Answering probabilistic queryExplanation
Bayes' rule can be used in answering probabilistic queries. It is a fundamental principle in probability theory that allows us to update our beliefs about an event based on new evidence. By calculating the conditional probability of an event given some evidence, Bayes' rule helps us make more accurate predictions and decisions in situations where uncertainty and probabilities are involved.Rate this question:
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- 2.
What does the bayesian network provides?
- A.
Complete description of the domain
- B.
Partial description of the domain
- C.
Complete description of the problem
- D.
None of the mentioned
Correct Answer
A. Complete description of the domainExplanation
A Bayesian network provides a complete description of the domain because it represents the relationships between variables and their probabilistic dependencies. It captures all the relevant information about the domain, including the variables, their conditional dependencies, and the probabilities associated with each variable. This allows for a comprehensive understanding of the domain and enables reasoning and inference based on the available data.Rate this question:
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- 3.
How the bayesian network can be used to answer any query?
- A.
Full distribution
- B.
Joint distribution
- C.
Partial distribution
- D.
All of the mentioned
Correct Answer
B. Joint distributionExplanation
A Bayesian network can be used to answer any query by utilizing the concept of joint distribution. The joint distribution represents the probability distribution of all variables in the network. By calculating the joint distribution, we can obtain the probability of any specific combination of variables or query. This allows us to answer queries related to any variable or set of variables in the network, making the joint distribution a powerful tool for inference in Bayesian networks.Rate this question:
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- 4.
What is the consequence between a node and its predecessors while creating bayesian network?
- A.
Functionally dependent
- B.
Dependant
- C.
Conditionally independent
- D.
Both Conditionally dependant & Dependant
Correct Answer
C. Conditionally independentExplanation
The consequence between a node and its predecessors while creating a Bayesian network is that they are conditionally independent. This means that the probability distribution of a node is only dependent on the values of its parents in the network and not on any other nodes in the network. This property allows for efficient computation and inference in Bayesian networks.Rate this question:
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- 5.
A _________ is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility.
- A.
Decision tree
- B.
Graphs
- C.
Trees
- D.
Neural Networks
Correct Answer
A. Decision treeExplanation
A decision tree is a decision support tool that uses a tree-like graph or model to represent decisions and their possible consequences. It considers chance event outcomes, resource costs, and utility in order to assist in decision-making. Decision trees are commonly used in various fields such as business, finance, and data analysis to visually represent and analyze complex decision-making processes.Rate this question:
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- 6.
Decision Tree is
- A.
Flow-Chart
- B.
Structure in which internal node represents test on an attribute, each branch represents outcome of test and each leaf node represents class label
- C.
Flow-Chart & Structure in which internal node represents test on an attribute, each branch represents outcome of test and each leaf node represents class label
- D.
None of the mentioned
Correct Answer
C. Flow-Chart & Structure in which internal node represents test on an attribute, each branch represents outcome of test and each leaf node represents class labelExplanation
A decision tree is a flow-chart-like structure in which each internal node represents a test on an attribute, each branch represents the outcome of the test, and each leaf node represents a class label. This means that at each internal node, a decision is made based on the value of a specific attribute, leading to different branches based on the possible outcomes of the test. Ultimately, the leaf nodes provide the final classification or class label for the given data.Rate this question:
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- 7.
Decision Trees can be used for Classification Tasks.
- A.
True
- B.
False
Correct Answer
A. TrueExplanation
Decision Trees are a popular machine learning algorithm that can be used for classification tasks. They work by creating a tree-like model of decisions and their possible consequences. Each internal node of the tree represents a decision based on a feature, and each leaf node represents a class label. By traversing the tree based on the values of the features, the algorithm can classify new instances into the appropriate class. Therefore, the statement that Decision Trees can be used for classification tasks is true.Rate this question:
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- 8.
Following are the advantage/s of Decision Trees. Choose that apply.
- A.
Possible Scenarios can be added
- B.
Use a white box model, If given result is provided by a model
- C.
Worst, best and expected values can be determined for different scenarios
- D.
All of the mentioned
Correct Answer
D. All of the mentionedExplanation
Decision trees have several advantages. Firstly, they can be considered as a white box model, meaning that the decision-making process is transparent and easily interpretable. This is beneficial as it allows users to understand how the model arrived at a certain decision. Secondly, decision trees can be used to determine worst, best, and expected values for different scenarios. By considering different possible outcomes, decision makers can make more informed choices. Therefore, all of the mentioned advantages apply to decision trees.Rate this question:
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- 9.
Decision Tree is a display of an algorithm.
- A.
True
- B.
False
Correct Answer
A. TrueExplanation
A decision tree is a graphical representation of an algorithm that uses a tree-like model of decisions and their possible consequences. It is a visual representation that helps in understanding and interpreting the decision-making process. Therefore, the statement "Decision Tree is a display of an algorithm" is true because it accurately describes the purpose and nature of a decision tree.Rate this question:
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- 10.
A decision tree is built in _______ fashion
- A.
Top-down
- B.
Bottom-up
- C.
By both top-down and bottom-up
- D.
Can't say
Correct Answer
A. Top-downExplanation
A decision tree is built in a top-down fashion, starting from the root node and recursively splitting the data based on the most informative features. This process continues until a stopping criterion is met, such as reaching a maximum depth or a minimum number of samples in each leaf node. The top-down approach allows for easy interpretation and understanding of the decision tree, as the most important features are considered first and the tree structure is built step by step.Rate this question:
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