Machine Learning 99+ Most Important MCQ (Multi choice question)
This Blog cover all possible Multi Choice Question from topic Introduction to Machine Learning, Concept Learning, Decision Tree.
Total amount of question covers in This MCQ series is 100. They cover all the important aspect related to that topic provided below.
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Machine Learning 99+ Most Important MCQ part2
Q 1. Which of the following feature need to be specify to well define the learning problem?
- A class of Task
- Measure of performance to be improved
- Source of Experience
- All of the Above
Correct answer
All of the Above
Q 2. The Type of Training experience available can have significant impact on success or failure of learning
Yes
No
Correct answer
- Predicting the outcome of a cricket match as win or loss based on historical data.
- Recommending a movie to an exisiting user on a website like IMdB based on the search history (including other users).
- Predicting the gender of a person from his/her image. You are given the data of 1 Million images along the gender
- Given the class labels of old news articles, predicting the class of a new news article from its content. Class of a news article can be such as sports, politics, technology, etc.
All of the Above
- Predict the age of a person
- Predict the country from where the person comes from
- Predict whether the price of petroleum will increase tomorrow
- Predict whether a document is related to science
Q 5 Which of the following are classification problems?
- Predicting the temperature (in Celsius) of a room from other environmental features (such as atmospheric pressure, humidity etc).
- Predicting if a cricket player is a batsman or bowler given his playing records.
- Finding the shorter route between two existing routes between two points.
- Predicting if a particular route between two points has traffic jam or not based on the travel time of vehicles.
Correct answer
- Predicting if a cricket player is a batsman or bowler given his playing records.
- Finding the shorter route between two existing routes between two points.
Feedback
-
Predicting the amount of rainfall based on various cues.
-
Detecting fraudulent credit card transactions
-
Training a robot to solve a maze
-
All of the above
Correct answer
Detecting fraudulent credit card transactions
Feedback
We can think of the task of detecting fraudulent credit card transactions as essentially representing all credit card transactions using some features and performing clustering. The majority of the transactions will be legal and come under one (or perhaps more) cluster whereas we hope to find transactions not in the above cluster to indicate fraudulent activity which can be detected. Predicting the amount of rainfall is essentially a supervised learning problem.
Q 7 Which of the following is a regression task?
- Predicting the monthly sales of a cloth store in rupees.
- Predicting if a user would like to listen to a newly released song or not based on historical data
- Predicting the confirmation probability (in fraction) of your train ticket whose current status is waiting list based on
historical data. - Predicting if a patient has diabetes or not based on historical medical records.
Correct answers
Predicting the monthly sales of a cloth store in rupees. Predicting the confirmation probability (in fraction) of your train ticket whose current status is waiting list based on historical data.
Feedback
Right answer is Predicting the monthly sales of a cloth store in rupees. Predicting the confirmation probability (in fraction) of your train ticket whose current status is waiting list based on historical data. both the example give a value as a outcome not the classification but in Predicting if a user would like to listen to a newly released song or not based on historical data. we are trying to classifies person like to listen newly released song or not. and Predicting if a patient has diabetes or not based on historical medical records. in this example we are trying to classifies patient on the bases of previous labelled data.
Q 8 Which of the following is a categorical feature?
- Number of legs of an animal
- Number of hours you study in a day
- Branch of an engineering student
- Your weekly expenditure in rupees.
Correct answer
Branch of an engineering student
Feedback
Right Answer is Branch of an engineering student Categorical Data is the data that generally takes a limited number of possible values. Also, the data in the category need not be numerical, it can be textual in nature.
Q 9 What is Machine learning?
- The autonomous acquisition of knowledge through the use of computer programs
- The autonomous acquisition of knowledge through the use of manual programs
- The selective acquisition of knowledge through the use of computer programs
- The selective acquisition of knowledge through the use of manual programs
Correct answer
The autonomous acquisition of knowledge through the use of computer programs
Feedback
Machine learning is the autonomous acquisition of knowledge through the use of computer programs. Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves. The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.
Q 10 Which of the factors affect the performance of learner system does not include?
- Training scenario
- Representation scheme used
- Good data structures
- Type of feedback
Correct answer
Good data structures
Q 11 Which one is most important supervised algorithm
- K-nears neighbors
- Linear Regression and Logistic Regression
- Neural networks
- Support Vector Machine
- Decision Tree and Random forest
- All of the above
Correct answer
All of the Above
Q 12 Which one is most important unsupervised algorithms
- clustering: k-mean, hierarchical cluster analysis
- Association Rule Learning: Eclat, apriori
- Kernel PCA & PCA
- t-Distributed
- All of the above
Correct answer
All of the Above
Q 13 How to solve Over fitting problem
- By Gathering more data for “Training Data“
- Reduce the noise level
- By selecting one with fewer parameters
- All of the above
Correct answer
All of the Above
Q 14 we can able to solve the Under fitting the Data by
- Selecting the most powerful model, which has many parameters
- Feeding the best feature into the algorithm mean feature Engineering
- Reduce the constraints on your model
- All of the above
Correct answer
All of the above
Q 14 Over fitting problem occurs when the model is very complex for the amount of training data given
True
False
Correct answer
True
Q 16 who is the father of Machine learning
- Geoffrey Chaucer
- Geoffrey Everest Hinton
- Geoffrey Hill
- None of the above Answer
Geoffrey Everest Hinton
Q 17 Which of the following does not include different learning methods
- Analogy
- Memorization
- Introduction
- Deduction Answer
Correct answer
Introduction
Q 18 Which of the attribute is responsible for choosing Training Experience?
- Type of training experience from which our system will learn
- The degree to which the learner controls the sequence of training examples
- How well it represents the distribution of examples over which the final system performance P must be measured
- All of the above
Correct answer
All of the above
Q 19 Learning from direct training feedback is easier than learning from indirect feedback.
- True
- False
Correct answer
True
Q 20 In language understanding, the levels of knowledge that does not include?
- Phonological knowledge
- Syntactic knowledge
- Logical knowledge
- Empirical knowledge
Correct answer
Empirical knowledge
Feedback
Phonological Knowledge is a broad skill that includes identifying and manipulating units of language such as words, syllables, and onsets and rimes. Phonemic awareness refers to the specific ability to focus on and manipulate individual sounds (phonemes) in spoken words. Syntactic knowledge is the knowledge of how words can be combined in meaningful
sentences, phrases, or utterances.
Q 21 Designing a machine learning approach involves
- Choosing the target function to be learned
- Choosing a function approximation algorithm
- Choosing the type of training experience
- Choosing a representation for the target function
- All of the above
Correct answer
All of the above
Q 22 Machine learning is the sub set of AI and deep learning is the subset of ML
True
False
Correct answer
True
Q 23 Which of the following is not type of learning?
- Supervised Learning
- Reinforcement Learning
- Unsupervised Learning
- Semi-unsupervised Learning
Correct answer
Semi-unsupervised Learning
Q 24 Concept learning inferred a _____valued function from training examples of its input and output.
- Decimal
- Boolean
- Hexadecimal
- All of the above
Correct answer
Boolean
Feedback
Concept learning. Inferring a boolean-valued function from training
examples of its input and output.
Q 25 For each attribute, the hypothesis indicates by a “?’ means
- That no value is acceptable
- That any value is acceptable for this attribute
- Specify a single required value for the attribute
- None of the above
Correct answer
That any value is acceptable for this attribute
Feedback
For each attribute, the hypothesis will either indicate by a “?’ that any value is acceptable for this attribute, specify a single required value (e.g., Warm) for the attribute, or indicate by a “0” that no value is acceptable.
Q 26 Concept learning can be viewed as the task of searching through a large space of hypotheses implicitly defined by the hypothesis representation. The goal of this search is to find the hypothesis that best fits the training examples.
- True
- False
True
a. 
b. 
c. 
d. 
Correct answer
Q 27 FIND-S Algorithm starts from the most specific hypothesis and generalize it by considering only
- Negative and Positive training examples
- Negative training examples
- Negative or Positive training examples
- Positive training examples
Correct answer
Positive training examples
Q 28 Which of the following are limitation of FIND-S Algorithm?
- It has no way to determine whether it has found the only hypothesis in H consistent with the data (i.e., the correct target concept), or whether there are many other consistent hypotheses as well
- Inconsistent sets of training examples can mislead FIND-S
- we can define hypothesis spaces for which there is no maximally specific consistent hypothesis perform poorly when given noisy training data
- All of the above
Correct answer
All of the above
Q 29 The CANDIDATE-ELIMINATION algorithm has been applied to problems such as learning regularities in chemical mass spectroscopy (Mitchell 1979) and learning control rules for heuristic search (Mitchell et al. 1983)
- True
- False
Correct answer
True
Q 30 The CANDIDATE-ELIMINATION algorithm represents the set of all hypotheses consistent with the observed training examples. This subset of all hypotheses is called
- Elimination Space
- Solution Space
- Version Space
- All of the above
Correct answer
Version Space
Q 31 CANDIDATE-ELIMINATION algorithm correctly describes the target concept when
- There are no errors in the training examples
- There is some hypothesis in H that correctly describes the target concept.
- When sufficient training examples have been observed
- All of the above
Correct answer
All of the above
Q 32 if the training data contains error Unfortunately, in this case the algorithm is certain to remove the correct target concept from the version space!
- True
- False
Correct answer
True
Q 33 Inductive learning is based on the knowledge that if something happens a lot it is likely to be
- True
- False
True
Q 34 Inductive learning takes examples and generalizes rather than starting with
- Existing
- Deductive
- Inductive
- None of these
Q 35 The version space of alternative hypotheses can be examined
- to determine whether the learner has converged to the target concept
- to determine when the training data are inconsistent
- to generate informative queries to further refine the version space
- to determine which unseen instances can be unambiguously classified based on the partially learned concept.
- All of the above
Correct answer
All of the above
Q 36 The FINDS algorithm utilizes this general-to-specific ordering, performing a specific-to-general search through the hypothesis space along one branch of the partial ordering, to find the most specific hypothesis consistent with the training examples.
- True
- False
Correct answer
True
Q 37 Which of the following is a widely used and effective machine learning algorithm based on the idea of bagging?
- Regression
- Decision Tree
- Random Forest
- Classification
Correct answer
Random Forest
Q 38 What strategies can help reduce over fitting in decision trees?
- Enforce a maximum depth for the tree
- Enforce a minimum number of samples in leaf nodes
- Pruning
- All of the above
Correct answer
All of the above
Q 39 CANDIDATE-ELIMINATION algorithm is not robust to noisy data or to situations in which the unknown target concept is not expressible in the provided hypothesis space.
- True
- False
Correct answer
True
Q 40 Inductive learning algorithms are able to classify unseen examples only because of their
- explicit inductive bias for selecting one consistent hypothesis over another
- implicit deductive bias for selecting one consistent hypothesis over another
- implicit inductive bias for selecting one consistent hypothesis over another
- None of them
Correct answer
implicit inductive bias for selecting one consistent hypothesis over another
Q 41 The bias associated with the CANDIDATE-ELIMINATION algorithm is that the target concept can be found in
- the provided hypothesis space
- the non provided hypothesis space
- the provided and non provided hypothesis space
- none of the above
Correct answer
the provided hypothesis space
Q 42 If the hypothesis space is enriched to the point where there is a hypothesis corresponding to every possible subset of instances (the power set of the instances), this will remove any inductive bias from the CANDIDATE ELIMINATION algorithm.
- True
- False
Correct answer
True
Q 43 who provided an early study of concept learning in humans
- Hunt and Hovland
- Winston’s
- Bruner
- Plotkin
Correct answer
Bruner
Feedback
Bruner et al. (1957) provided an early study of concept learning in humans
Q 44 who put an early effort to automate Concept learning
- Bruner
- Winston’s
- Plotkin
- Hunt and Hovland
Hunt and Hovland
Feedback
Hunt and Hovland (1963) put an early effort to automate concept learning
Q 45 who provided an early formalization of the more-general-than relation, as well as the related notion of theta-subsumption
- Plotkin
- Simon and Lea
- Winston
- Bruner
Correct answer
Plotkin
Feedback
Plotkin (1970, 1971) provided an early formalization of the more-general-than relation,
as well as the related notion of theta-subsumption
Q 46 Version spaces and the CANDIDATE-ELIMINATION algorithm were introduced by
- Haussler
- Mitchell
- Smith and Rosenbloom
- Subramanian and Feigenbaum
Correct answer
Mitchell
Feedback
Version spaces and the CANDIDATE-ELIMINATION algorithm were introduced by Mitchell (1977, 1982)
Q 47 who shows that the size of the general boundary can grow exponentially in the number of training examples, even when the hypothesis space consists of simple conjunctions of features
- Haussler
- Mitchell
- Smith and Rosenbloom
- Subramanian and Feigenbaum
Correct answer
Haussler
Q 48 One of the greatest practical limitations of the CANDIDATE ELIMINATION algorithm is that it requires
- Noise-free training data
- Noise-full training data
- Dose not matter
- none of the them
Correct answer
Noise-free training data
Q 49 Algorithm that generalizes the CANDIDATE-ELIMINATION algorithm to handle situations in which training information can be different types of constraints represented using version spaces
- INCREMENTAL VERSION SPACE MERGING Algorithm
- FIND – S Algorithm
- LIST-THEN-ELIMINATE Algorithm
- None of the above
Correct answer
INCREMENTAL VERSION SPACE MERGING Algorithm
Q 50 Which of the following is an unsupervised task?
- Grouping images of footwear and caps separately for a given set of images.
- Learning to play chess.
- Predicting if an edible item is sweet or spicy based on the information of the ingredients and their quantities.
- all of the above
Correct answer
Grouping images of footwear and caps separately for a given set of images.
Q 51 To find the minimum or the maximum of a function, we set the gradient to zero because which of the following
- Depends on the type of problem
- The value of the gradient at extrema of a function is always zero
- Both (A) and (B)
- None of these Answer
Correct answer
The value of the gradient at extrema of a function is always zero
Q 52 Machine learning algorithms are especially useful in
- Data mining problems where large databases may contain valuable implicit regularities that can be discovered automatically (e.g., to analyze outcomes of medical treatments from patient databases or to learn general rules for credit worthiness from financial databases) Poorly understood domains where human might not have the knowledge needed to develop effective algorithms (e.g., human face recognition from images)
- Domains where the program must dynamically adapt to changing conditions
- All of the above
Correct answer
All of the above
Q 53 Real-Time decisions, Game AI, Learning Tasks, Skill Acquisition, and Robot Navigation are applications of which of the following
- Unsupervised Learning: Clustering
- Supervised Learning: Regression
- Supervised Learning: Classification
- Reinforcement Learning
Correct answer
Reinforcement Learning
Q 54 Decision tree learning is a method for approximating _________valued functions that is robust to noisy data and capable of learning disjunctive expressions.
- Decimal
- Discrete
- Hexadecimal
- All of the above
Correct answer
Discrete
Q 55 Which of the following characteristics of Problems best suits the Decision tree learning Problem
- Instances are represented by attribute-value pairs.
- The target function has discrete output values.
- Disjunctive descriptions is required.
- The training data may contain missing attribute values
- All of the above
Correct answer
All of the above
Q 56 In ID3 Decision Tree algorithm the entropy is 0 if
- all members of sample data belong to the same class
- all members of sample data belong to the different class
- all members of sample data belong to the same class are not specified
- all of the above
all members of sample data belong to the same class
Q 57 Which of the following is a disadvantage of decision trees?
- Decision trees are prone to be overfit
- Decision trees are robust to outliers
- Factor analysis
- None of the above
Correct answer
Decision trees are prone to be overfit
Q 58 The Entropy is 1 when
- the collection contains an equal number of positive and negative examples
- the collection contains an unequal number of positive and negative examples
- not predictable
- none of the above
Correct answer
the collection contains an equal number of positive and negative examples
Q 59 The Entropy is between 0 and 1 when
- the collection contains an equal number of positive and negative examples
- the collection contains an unequal number of positive and negative examples
- not predictable
- none of the above
Correct answer
the collection contains an unequal number of positive and negative examples
Q 60 Statistical property, that measures how well a given attribute separates the training examples according to their target classification is called
- Entropy
- Information gain
- Information gain and Entropy both
- none of the above
Correct answer
Information gain
Q 61 which one is the write formula for Entropy
Correct answer
Q 62 ID 3 performs a simple-to-complex _____________search through hypothesis space
- hill-climbing
- Linear search
- depth first search
- none of the above
hill-climbing
Q 63 ID 3 in its pure form performs no backtracking in its search
- True
- False
Correct answer
True
Q 64 Inductive bias is the set of assumptions that, together with the training data, deductively justify the classifications assigned by the learner to future instances
- True
- False
Correct answer
True
Q 65 Approximate inductive bias of ID 3 is
- Larger trees are preferred over Shorten trees
- Shorter trees are preferred over larger trees
- Both shorter and Longer Tree are preferred
- None of the above
Correct answer
Shorter trees are preferred over larger trees
Q 66 Consider the following set of training examples:
- a) 0 & b) 0.9
- a) 1 & b) 0
- a) 1 & b) 0.1
- a) 0 & b) 1
Correct answer
a) 1 & b) 0.1
Feedback
Q 67 The information gain, Gain(S, A) of an attribute A, relative to a collection of examples S, is defined as
- True
- False
True
Q 68 Which one is correct formula for finding Entropy for S
- Option A only
- Option B only
- Option A & Option B both
- none of the above
Q 69 Methods for post-pruning the decision tree is important to avoid over-fitting in decision tree learning
- True
- False
True
- Methods for post-pruning trees
- Handling real-valued attributes
- Accommodating training examples with missing attribute values
- considering costs associated with instance attributes
- All of the above
Correct answer
All of the above
Q 71 Earliest work on decision tree learning is
- Hunt’s Concept Learning System (CLS)
- Quinlan’s ID3 system
- Hunt’s Concept Learning System (CLS) and Quinlan’s
ID3 system both - none of the above
Correct answer
Hunt’s Concept Learning System (CLS) and Quinlan’s
ID3 system both
Q 72 What would be the ideal complexity of the curve which can be used for separating the two classes shown in the image below.
- Linear
- Quadratic
- Cubic
- insufficient
data to draw conclusion
Correct answer
Quadratic
Feedback
For the 2D data points shown in the figure, the distribution of the data suggests that the ideal complexity of the curve used for separating the two classes is quadratic. A linear boundary would result in a large number of miss-classifications, whereas a third degree curve would not do any better than the quadratic.
Q 73 What would be the ideal complexity of the curve which can be used for separating the two classes shown in the image below?
- Linear
- Quadratic
- Cubic
- insufficient data to draw conclusion
Correct answer
Linear
Feed Back
Reason is same
Q 74 Which of the following is correct about the Naive Bayes?
- Assumes that all the features in a data set are independent
- Assumes that all the features in a data set are equally important
- Both of them
- None of the above
Correct answer
- Both of them
Q 75 Which of the following is/are Examples of Naïve Bayes Algorithm
- Spam filtration
- Sentimental analysis
- Classifying articles
- All of the above
Correct answer
All of the above
Q 76 Which of the following is disadvantages of Naive Bayes Classifier?
- Naive Bayes assumes that all features are independent or unrelated
- It performs well in Multi-class predictions as compared to the other
- Naive Bayes is one of the fast and easy ML algorithms to predict a class of
- It is the most popular choice for text classification problems.
Correct answer
Naive Bayes assumes that all features are independent or unrelated
Q 77 Occam’s razor is an example of:
- Inductive bias
- Preference bias
- Inductive bias & Preference bias both
- none of the above
Correct answer
Preference bias
Q 78 How does generalization performance change with increasing size of training set?
- Improves
- Deteriorates
- No Change
- None of the above
Correct answer
Improves
Q 79 What is the Full form of MDL
- Minimum Description Length
- Maximum Description Length
- Minimum Domain Length
- None of these Answer
Correct answer
Minimum Description Length
Q 80 Which of the following is / are benefit of Naive Bayes
- Naive Bayes is one of the fast and easy ML algorithms to predict a class of data
- It is the most popular choice for text classification problems.
- It can be used for Binary as well as Multi-class
- All of the above
All of the above
- 3
- 2
- 1
- 4
Correct answer
3 layers
Q 82 The neurons in the hidden layer contains Gaussian transfer function whose output are to the distance from the center of the neuron.
- Directly
- Inversely
- equal
- None of these
Correct answer
Inversely
Q 83 The ID 3 family of algorithms infers decision trees by growing them from the root downward, greedily selecting the next best attribute for each new decision branch added to the tree.
- True
- False
Correct answer
True
Q 84 To avoid this difficulty is to select decision attributes based on some measure other than information gain. One alternative measure that has been used successfully is the gain ratio
- True
- False
Correct answer
True
- PNN/GRNN
- RBF
- K-means clustering
- None of these
Correct answer
PNN/GRNN
Q 86 What are the main advantages of converting the decision tree to rules before pruning are
- Converting to rules allows distinguishing among the different contexts in which a decision node is used.
- Converting to rules removes the distinction between attribute tests that occur near the root of the tree and those that occur near
the leaves - Converting to rules improves readability. Rules are often easier for to understand.
- All of the above
Correct answer
All of the above
Q 87 Converting to rules allows distinguishing
among the different contexts in which a decision node is used because
- Each distinct path through the decision tree node produces a distinct rule
- The pruning decision regarding that attribute test can be made differently for each path
- Both A and B
- None of the above
Correct answer
Both A and B
Feedback
Converting to rules allows distinguishing among the different contexts in which a decision node is used. Because each distinct path through the decision tree node produces a distinct rule, the pruning decision regarding that attribute test can be made differently for each path.
Q 88 What is/are advantages of CBR?
- A local approx. is found for each test case
- Knowledge is in a form understandable to human
- Fast to train
- All of the above
Correct answer
All of the above
Q 89 Rule post-pruning involves the Which of the following steps:
- Infer the decision tree from the training set, growing the tree until the training data is fit as well as possible and allowing over fitting to occur.
- Convert the learned tree into an equivalent set of rules by creating one rule for each path from the root node to a leaf node.
- Prune (generalize) each rule by removing any preconditions that result in improving its estimated accuracy.
- Sort the pruned rules by their estimated accuracy, and consider them in this sequence when classifying subsequent instances.
- All of the above
Correct answer
All of the above
Q 90 random noise in the training examples can lead to over fitting
- True
- False
Correct answer
True
Q 91 Which of the approaches is used to avoiding over fitting in decision tree learning
- Stop growing the tree earlier, before it reaches the point where it perfectly classifies the training data.
- Allow the tree to over fit the data, and then post-prune the tree.
- Both A and B
- None of the Above
Correct answer
Both A and B
Q 92 what are the various used to determine the correct final tree size
- Use a separate set of examples, distinct from the
training examples, to evaluate the utility of post-pruning nodes from the tree. - Use all the available data for training
- Use an explicit measure of the complexity for
encoding the training examples and the decision tree, halting growth of the
tree when this encoding size is minimized. - All of the above
Correct answer
All of the above
Q 93 which of the following option is correct,
Statement: ID s hypothesis space of all decision trees is a complete space of finite discrete-valued functions, relative to the available attributes.
Reason: every finite discrete-valued function can be represented by some decision tree
- Statement is Correct but Reason is Wrong
- Statement is Wrong but Reason is Correct
- both Statement & Reason is Correct
- both Statement & Reason is Wrong
Correct answer
both Statement & Reason is Correct
Q 94 ID 3 avoids one of the major risks of methods that search incomplete hypothesis spaces
- True
- False
Correct answer
True
Q 95 which of the following option is correct ,
Statement : It is susceptible to the usual risks of converging to locally optimal solutions that are not globally optimal.
Reason: ID3 in its pure form performs no backtracking in its search
- Statement is Correct but Reason is Wrong
- Statement is Wrong but Reason is Correct
- both Statement & Reason is Correct
- both Statement & Reason is Wrong
Correct answer
both Statement & Reason is Correct
Q 96 Which of the following is not function of symbolic in the various function representation of Machine Learning?
- Hidden-Markov Models (HMM)
- Rules in proportional Logic
- Rules in first-order predicate logic
- Decision Trees Answer
Correct answer
Hidden-Markov Models (HMM)
Q 97 Which of the following is not numerical functions in the various function representation of Machine Learning?
- Case-based
- Neural Network
- Linear Regression
- Support Vector Machines
Correct answer
Case-based
Q 98 Split information is actually the entropy of S with respect to the values of attribute A
- True
- False
Correct answer
True
Q 99 distance-based measure which solve the problem of Gain ratio introduced by
- Lopez de Mantaras
- Mingers
- Kearns and Mansour
- Dietterich et
Correct answer
Lopez de Mantaras
Q 100 Which one is the right strategy for dealing with the missing attribute( A ) value
- Assign it the value that is most common among
training examples at node n - Assign a probability to each of the possible values
of A - Both A and B
- None of the above
Correct answer
Both A and B
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