Decision trees are appropriate for the problems where: a) Attributes are both numeric and nominal b) Target function takes on a discrete number of values. c) Data may have errors d) All of the mentioned

Decision trees are appropriate for the problems where:
a) Attributes are both numeric and nominal
b) Target function takes on a discrete number of values.
c) Data may have errors
d) All of the mentioned
by

1 Answer

Answer :

d) All of the mentioned
by

Related questions

Decision trees are appropriate for the problems where ___________ a) Attributes are both numeric and nominal b) Target function takes on a discrete number of values. c) Data may have errors d) All of the mentioned

Description : Decision trees are appropriate for the problems where ___________ a) Attributes are both numeric and nominal b) Target function takes on a discrete number of values. c) Data may have errors d) All of the mentioned

Last Answer : d) All of the mentioned

Which of the following are the advantage/s of Decision Trees? 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

Description : Which of the following are the advantage/s of Decision Trees? 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

Last Answer : d) All of the mentioned

What takes input as an object described by a set of attributes? a) Tree b) Graph c) Decision graph d) Decision tree

Description : What takes input as an object described by a set of attributes? a) Tree b) Graph c) Decision graph d) Decision tree

Last Answer : d) Decision tree

What takes input as an object described by a set of attributes? a) Tree b) Graph c) Decision graph d) Decision tree

Description : What takes input as an object described by a set of attributes? a) Tree b) Graph c) Decision graph d) Decision tree

Last Answer : d) Decision tree

The values of the set membership is represented by ___________ a) Discrete Set b) Degree of truth c) Probabilities d) Both Degree of truth & Probabilities

Description : The values of the set membership is represented by ___________ a) Discrete Set b) Degree of truth c) Probabilities d) Both Degree of truth & Probabilities

Last Answer : b) Degree of truth

The values of the set membership is represented by a) Discrete Set b) Degree of truth c) Probabilities d) Both b & c

Description : The values of the set membership is represented by a) Discrete Set b) Degree of truth c) Probabilities d) Both b & c

Last Answer : b) Degree of truth

Quantitative attributes are A. A reference to the speed of an algorithm, which is quadratically dependent on the size of the data B. Attributes of a database table that can take only numerical values C. Tools designed to query a database D. None of these

Description : Quantitative attributes are A. A reference to the speed of an algorithm, which is quadratically dependent on the size of the data B. Attributes of a database table that can take only numerical values C. Tools designed to query a database D. None of these

Last Answer : B. Attributes of a database table that can take only numerical values

Which variable cannot be written in entire distribution as a table? a) Discrete b) Continuous c) Both Discrete & Continuous d) None of the mentioned

Description : Which variable cannot be written in entire distribution as a table? a) Discrete b) Continuous c) Both Discrete & Continuous d) None of the mentioned

Last Answer : b) Continuous

Which of the following is the model used for learning? a) Decision trees b) Neural networks c) Propositional and FOL rules d) All of the mentioned

Description : Which of the following is the model used for learning? a) Decision trees b) Neural networks c) Propositional and FOL rules d) All of the mentioned

Last Answer : d) All of the mentioned

How does the state of the process is described in HMM? a) Literal b) Single random variable c) Single discrete random variable d) None of the mentioned

Description : How does the state of the process is described in HMM? a) Literal b) Single random variable c) Single discrete random variable d) None of the mentioned

Last Answer : c) Single discrete random variable

People overcome natural language problems by _____________ a) grouping attributes into frames b) understanding ideas in context c) identifying with familiar situations d) both understanding ideas in context & identifying with familiar situations

Description : People overcome natural language problems by _____________ a) grouping attributes into frames b) understanding ideas in context c) identifying with familiar situations d) both understanding ideas in context & identifying with familiar situations

Last Answer : d) both understanding ideas in context & identifying with familiar situations

Which of the following function returns t if the object is a number in LISP? a) (number <object>) b) (numberp <object>) c) (numericp <object>) d) (numeric <object>)

Description : Which of the following function returns t if the object is a number in LISP? a) (number ) b) (numberp ) c) (numericp ) d) (numeric )

Last Answer : b) (numberp )

What is meant by probability density function? a) Probability distributions b) Continuous variable c) Discrete variable d) Probability distributions for Continuous variables

Description : What is meant by probability density function? a) Probability distributions b) Continuous variable c) Discrete variable d) Probability distributions for Continuous variables

Last Answer : d) Probability distributions for Continuous variables

Membership function defines the fuzziness in a fuzzy set irrespective of the elements in the set, which are discrete or continuous. A. True B. False

Description : Membership function defines the fuzziness in a fuzzy set irrespective of the elements in the set, which are discrete or continuous. A. True B. False

Last Answer : B. False

What is meant by probability density function? a) Probability distributions b) Continuous variable c) Discrete variable d) Probability distributions for Continuous variables

Description : What is meant by probability density function? a) Probability distributions b) Continuous variable c) Discrete variable d) Probability distributions for Continuous variables

Last Answer : d) Probability distributions for Continuous variables

The Set of actions for a problem in a state space is formulated by a ___________ a) Intermediate states b) Initial state c) Successor function, which takes current action and returns next immediate state d) None of the mentioned

Description : The Set of actions for a problem in a state space is formulated by a ___________ a) Intermediate states b) Initial state c) Successor function, which takes current action and returns next immediate state d) None of the mentioned

Last Answer : c) Successor function, which takes current action and returns next immediate state

Which variable can give the concrete form to the representation of the transition model? a) Single variable b) Discrete state variable c) Random variable d) Both Single & Discrete state variable

Description : Which variable can give the concrete form to the representation of the transition model? a) Single variable b) Discrete state variable c) Random variable d) Both Single & Discrete state variable

Last Answer : d) Both Single & Discrete state variable

A Hybrid Bayesian network contains ___________ a) Both discrete and continuous variables b) Only Discrete variables c) Only Discontinuous variable

Description : A Hybrid Bayesian network contains ___________ a) Both discrete and continuous variables b) Only Discrete variables c) Only Discontinuous variable

Last Answer : a) Both discrete and continuous variables

The minimax algorithm computes the minimax decision from the current state. It uses a simple recursive computation of the minimax values of each successor state, directly implementing the defining equations. The recursion proceeds all the way down to the leaves of the tree, and then the minimax values are backed up through the tree as the recursion unwinds. a) True b) False

Description : The minimax algorithm computes the minimax decision from the current state. It uses a simple recursive computation of the minimax values of each successor state, directly implementing the defining equations. The ... are backed up through the tree as the recursion unwinds. a) True b) False

Last Answer : a) True

extendible architecture is A. Modular design of a software application that facilitates the integration of new modules B. Showing a universal law or rule to be invalid by providing a counter example C. A set of attributes in a database table that refers to data in another table D. None of these

Description : extendible architecture is A. Modular design of a software application that facilitates the integration of new modules B. Showing a universal law or rule to be invalid by providing a counter example C. ... of attributes in a database table that refers to data in another table D. None of these

Last Answer : A. Modular design of a software application that facilitates the integration of new modules

Falsification is A. Modular design of a software application that facilitates the integration of new modules B. Showing a universal law or rule to be invalid by providing a counter example C. A set of attributes in a database table that refers to data in another table D. None of these

Description : Falsification is A. Modular design of a software application that facilitates the integration of new modules B. Showing a universal law or rule to be invalid by providing a counter example C. A set of attributes in a database table that refers to data in another table D. None of these

Last Answer : B. Showing a universal law or rule to be invalid by providing a counter example

What are the two subfields of Natural language processing? a) symbolic and numeric b) time and motion c) algorithmic and heuristic d) understanding and generation

Description : What are the two subfields of Natural language processing? a) symbolic and numeric b) time and motion c) algorithmic and heuristic d) understanding and generation

Last Answer : c) algorithmic and heuristic

Which of the following is true in Statistical reasoning? a) The representation is extended to allow some kind of numeric measure of certainty to be associated with each statement b) The representation is extended to allow ‘TRUE or FALSE’ to be associated with each statement c) The representation is extended to allow some kind of numeric measure of certainty to be associated common to all statements

Description : Which of the following is true in Statistical reasoning? a) The representation is extended to allow some kind of numeric measure of certainty to be associated with each statement b) The ... extended to allow some kind of numeric measure of certainty to be associated common to all statements

Last Answer : a) The representation is extended to allow some kind of numeric measure of certainty to be associated with each statement

Decision Trees can be used for Classification Tasks. a) True b) False

Description : Decision Trees can be used for Classification Tasks. a) True b) False

Last Answer : a) True

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

Description : 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

Last Answer : a) Decision tree

Mention the exercises which broaden the applications of decision trees.

Description : Mention the exercises which broaden the applications of decision trees.

Last Answer : i. Missing data  ii. Multivalued attributes  iii. Continuous and integer valued input attributes  iv. Continuous valued output attributes.

Choose from the following that are Decision Tree nodes? a) Decision Nodes b) End Nodes c) Chance Nodes d) All of the mentioned

Description : Choose from the following that are Decision Tree nodes? a) Decision Nodes b) End Nodes c) Chance Nodes d) All of the mentioned

Last Answer : d) All of the mentioned

What is Decision Tree? 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

Description : What is Decision Tree? 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) ... branch represents outcome of test and each leaf node represents class label d) None of the mentioned

Last 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 label

Which modifies the performance element so that it makes better decision? a) Performance element b) Changing element c) Learning element d) None of the mentioned

Description : Which modifies the performance element so that it makes better decision? a) Performance element b) Changing element c) Learning element d) None of the mentioned

Last Answer : c) Learning element

Which of the following are the applications of Expert systems? a) Disease Diagnosis b) Planning and Scheduling c) Decision making d) All of the mentioned

Description : Which of the following are the applications of Expert systems? a) Disease Diagnosis b) Planning and Scheduling c) Decision making d) All of the mentioned

Last Answer : d) All of the mentioned

Which search is equal to minimax search but eliminates the branches that can’t influence the final decision? a) Depth-first search b) Breadth-first search c) Alpha-beta pruning d) None of the mentioned

Description : Which search is equal to minimax search but eliminates the branches that can’t influence the final decision? a) Depth-first search b) Breadth-first search c) Alpha-beta pruning d) None of the mentioned

Last Answer : c) Alpha-beta pruning

Which modifies the performance element so that it makes better decision? a) Performance element b) Changing element c) Learning element d) None of the mentioned

Description : Which modifies the performance element so that it makes better decision? a) Performance element b) Changing element c) Learning element d) None of the mentioned

Last Answer : c) Learning element

Which is an appropriate language for describing the relationships? a) First-order logic b) Propositional logic c) ILP d) None of the mentioned

Description : Which is an appropriate language for describing the relationships? a) First-order logic b) Propositional logic c) ILP d) None of the mentioned

Last Answer : a) First-order logic

Neural Networks are complex ______________ with many parameters. a) Linear Functions b) Nonlinear Functions c) Discrete Functions d) Exponential Functions

Description : Neural Networks are complex ______________ with many parameters. a) Linear Functions b) Nonlinear Functions c) Discrete Functions d) Exponential Functions

Last Answer : a) Linear Functions

Neural Networks are complex ______________with many parameters. a) Linear Functions b) Nonlinear Functions c) Discrete Functions d) Exponential Functions

Description : Neural Networks are complex ______________with many parameters. a) Linear Functions b) Nonlinear Functions c) Discrete Functions d) Exponential Functions

Last Answer : b) Nonlinear Functions

Neural Networks are complex ———————–with many parameters. a) Linear Functions b) Nonlinear Functions c) Discrete Functions d) Exponential Functions e) Power Functions

Description : Neural Networks are complex ———————–with many parameters. a) Linear Functions b) Nonlinear Functions c) Discrete Functions d) Exponential Functions e) Power Functions

Last Answer : b) Nonlinear Functions

Neural Networks are complex ______________ with many parameters. a) Linear Functions b) Nonlinear Functions c) Discrete Functions d) Exponential Functions

Description : Neural Networks are complex ______________ with many parameters. a) Linear Functions b) Nonlinear Functions c) Discrete Functions d) Exponential Functions

Last Answer : a) Linear Functions

Which values are independant in minimax search algorithm? a) Pruned leaves x and y b) Every states are dependant c) Root is independant d) None of the mentioned

Description : Which values are independant in minimax search algorithm? a) Pruned leaves x and y b) Every states are dependant c) Root is independant d) None of the mentioned

Last Answer : a) Pruned leaves x and y

Generality is the measure of _____________ a) Ease with which the method can be adapted to different domains of application b) The average time required to construct the target knowledge structures from some specified initial structures c) A learning system to function with unreliable feedback and with a variety of training examples d) The overall power of the system

Description : Generality is the measure of _____________ a) Ease with which the method can be adapted to different domains of application b) The average time required to construct the target knowledge structures ... unreliable feedback and with a variety of training examples d) The overall power of the system

Last Answer : a) Ease with which the method can be adapted to different domains of application

_______________ are mathematical problems defined as a set of objects whose state must satisfy a number of constraints or limitations. a) Constraints Satisfaction Problems b) Uninformed Search Problems c) Local Search Problems d) All of the mentioned

Description : _______________ are mathematical problems defined as a set of objects whose state must satisfy a number of constraints or limitations. a) Constraints Satisfaction Problems b) Uninformed Search Problems c) Local Search Problems d) All of the mentioned

Last Answer : a) Constraints Satisfaction Problems

_______ data have discretestates and take discrete values. A) Analog B) Digital C) (a) or (b) D) None of the above

Description : _______ data have discretestates and take discrete values. A) Analog B) Digital C) (a) or (b) D) None of the above

Last Answer : Digital

Most of the outcome/dependent variable characteristics and attributes measured on educational characteristics probably exist at the ______________ level of measurement. a. Nominal b. Ordinal c. Interval d. Ratio

Description : Most of the outcome/dependent variable characteristics and attributes measured on educational characteristics probably exist at the ______________ level of measurement. a. Nominal b. Ordinal c. Interval d. Ratio

Last Answer : b. Ordinal

Which cannot be represented by a set of attributes? a) Program b) Three-dimensional configuration of a protein molecule c) Agents

Description : Which cannot be represented by a set of attributes? a) Program b) Three-dimensional configuration of a protein molecule c) Agents

Last Answer : b) Three-dimensional configuration of a protein molecule

Constraint satisfaction problems on finite domains are typically solved using a form of ___________ a) Search Algorithms b) Heuristic Search Algorithms c) Greedy Search Algorithms d) All of the mentioned

Description : Constraint satisfaction problems on finite domains are typically solved using a form of ___________ a) Search Algorithms b) Heuristic Search Algorithms c) Greedy Search Algorithms d) All of the mentioned

Last Answer : d) All of the mentioned

Which of the Following problems can be modeled as CSP? a) 8-Puzzle problem b) 8-Queen problem c) Map coloring problem d) All of the mentioned

Description : Which of the Following problems can be modeled as CSP? a) 8-Puzzle problem b) 8-Queen problem c) Map coloring problem d) All of the mentioned

Last Answer : d) All of the mentioned

What will happen if the hypothesis space contains the true function? a) Realizable b) Unrealizable c) Both Realizable & Unrealizable d) None of the mentioned

Description : What will happen if the hypothesis space contains the true function? a) Realizable b) Unrealizable c) Both Realizable & Unrealizable d) None of the mentioned

Last Answer : b) Unrealizable

What will happen if the hypothesis space contains the true function? a) Realizable b) Unrealizable c) Both a & b d) None of the mentioned

Description : What will happen if the hypothesis space contains the true function? a) Realizable b) Unrealizable c) Both a & b d) None of the mentioned

Last Answer : b) Unrealizable

Which of the following elements constitutes the frame structure? a) Facts or Data b) Procedures and default values c) Frame names d) Frame reference in hierarchy

Description : Which of the following elements constitutes the frame structure? a) Facts or Data b) Procedures and default values c) Frame names d) Frame reference in hierarchy

Last Answer : a) Facts or Data

The crossover points of a membership function are defined as the elements in the universe for which a particular fuzzy set has values equal to A. infinite B. 1 C. 0 D. 0.5

Description : The crossover points of a membership function are defined as the elements in the universe for which a particular fuzzy set has values equal to A. infinite B. 1 C. 0 D. 0.5

Last Answer : D. 0.5

The membership values of the membership function are nor strictly monotonically increasing or decreasing or strictly monoronically increasing than decreasing. A. Convex Fuzzy Set B. Non convex fuzzy set C. Normal Fuzzy set D. Sub normal fuzzy set

Description : The membership values of the membership function are nor strictly monotonically increasing or decreasing or strictly monoronically increasing than decreasing. A. Convex Fuzzy Set B. Non convex fuzzy set C. Normal Fuzzy set D. Sub normal fuzzy set

Last Answer : B. Non convex fuzzy set