Cs- 1750 Ml Quiz 2

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Quizzes Created: 1 | Total Attempts: 100
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  • 1/10 Questions

    Decision Trees can be used for Classification Tasks.

    • True
    • False
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Cs- 1750 Ml Quiz 2 - Quiz
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  • 2. 

    Decision Tree is a display of an algorithm.

    • True

    • False

    Correct Answer
    A. True
    Explanation
    A decision tree is a graphical representation of an algorithm that uses a tree-like model to make decisions. It starts with a single node, known as the root, and then branches out into multiple nodes, known as internal nodes, which represent decision points. The branches represent the possible outcomes or decisions that can be made based on certain conditions or criteria. The leaves of the tree represent the final decisions or outcomes. Therefore, it can be said that a decision tree is indeed a display of an algorithm.

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  • 3. 

    Where does the bayes rule can be used?

    • Solving queries

    • Increasing complexity

    • Decreasing complexity

    • Answering probabilistic query

    Correct Answer
    A. Answering probabilistic query
    Explanation
    Bayes' rule can be used for answering probabilistic queries. It is a fundamental theorem in probability theory that allows us to update our beliefs or probabilities about an event based on new evidence or information. By using Bayes' rule, we can calculate the posterior probability of an event given prior knowledge and observed data. This makes it a valuable tool in various fields such as statistics, machine learning, and artificial intelligence, where probabilistic reasoning and inference are necessary.

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  • 4. 

     Decision Tree is

    • 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

    • 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

    • None of the mentioned

    Correct Answer
    A. 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
    Explanation
    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 that 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, and the outcome of that decision determines which branch to follow. The process continues until a leaf node is reached, which assigns a class label to the instance being classified.

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  • 5. 

    Following are the advantage/s of Decision Trees. Choose that apply.

    • Possible Scenarios can be added

    • Use a white box model, If given result is provided by a model

    • Worst, best and expected values can be determined for different scenarios

    • All of the mentioned

    Correct Answer
    A. All of the mentioned
    Explanation
    Decision Trees have several advantages. Firstly, they can be considered as a white box model, meaning that the decision-making process is transparent and easy to interpret. Additionally, Decision Trees allow for the determination of worst, best, and expected values for different scenarios, providing a comprehensive understanding of the potential outcomes. Therefore, all of the mentioned advantages apply to Decision Trees.

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  • 6. 

    A decision tree is built in _______ fashion

    • Top-down

    • Bottom-up

    • By both top-down and bottom-up

    • Can't say

    Correct Answer
    A. Top-down
    Explanation
    A decision tree is built in a top-down fashion. This means that the tree is constructed by starting at the root node and recursively partitioning the data based on the best attribute at each step. The process continues until a stopping criterion is met, such as reaching a maximum depth or purity level. This top-down approach allows for the creation of a hierarchical structure that is easy to interpret and provides insights into the decision-making process.

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  • 7. 

    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.

    • Decision tree

    • Graphs

    • Trees

    • Neural Networks

    Correct Answer
    A. Decision tree
    Explanation
    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 when making decisions. Decision trees are commonly used in various fields such as finance, business, and healthcare to analyze and visualize complex decision-making processes. They provide a structured approach to decision-making by breaking down a problem into a series of interconnected decisions and their potential outcomes, helping individuals or organizations make informed choices based on the available information.

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  • 8. 

    What does the bayesian network provides?

    • Complete description of the domain

    • Partial description of the domain

    • Complete description of the problem

    • None of the mentioned

    Correct Answer
    A. Complete description of the domain
    Explanation
    A Bayesian network provides a complete description of the domain. It is a graphical model that represents the probabilistic relationships among a set of variables. The network consists of nodes representing variables and directed edges representing dependencies between variables. By specifying the conditional probability distributions for each variable given its parents in the network, a complete description of the domain is obtained. This allows for reasoning and inference about the variables in the domain based on observed evidence.

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  • 9. 

    How the bayesian network can be used to answer any query?

    • Full distribution

    • Joint distribution

    • Partial distribution

    • All of the mentioned

    Correct Answer
    A. Joint distribution
    Explanation
    Bayesian networks are graphical models that represent probabilistic relationships between variables. They can be used to answer queries by calculating the joint distribution of the variables involved. The joint distribution provides the complete probability distribution of all variables in the network, allowing for accurate and comprehensive answers to queries. Therefore, the correct answer is "Joint distribution."

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  • 10. 

    What is the consequence between a node and its predecessors while creating bayesian network?

    • Functionally dependent

    • Dependant

    • Conditionally independent

    • Both Conditionally dependant & Dependant

    Correct Answer
    A. Conditionally independent
    Explanation
    When creating a Bayesian network, the consequence between a node and its predecessors is that they are conditionally independent. This means that the probability distribution of the node is only dependent on the values of its immediate predecessors in the network, and not on any other nodes in the network. Therefore, the node's value can be determined without considering the values of other nodes, making them conditionally independent.

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  • Aug 22, 2023
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  • Sep 19, 2019
    Quiz Created by
    TARUN
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