Discover important Machine Learning MCQs with answers to boost your knowledge and prepare effectively for exams and interviews in the field of Machine Learning.
Machine Learning MCQs with Answers
Regression trees are often used to model _____ data.
A. linear
B. nonlinear
C. categorical
D. symmetrical
Support Vector Machine is
A. logical model
B. probabilistic model
C. geometric model
D. none of the above
For the given weather data, Calculate probability of not playing
A. 0.4
B. 0.64
C. 0.36
D. 0.5
In PCA the number of input dimensiona are equal to principal components
A. TRUE
B. FALSE
What are the two methods used for the calibration in Supervised Learning?
A. Platt Calibration and Isotonic Regression
B. Statistics and Informal Retrieval
C. Both A and B
D. None of these
Regression trees are often used to model . . . . . . . . data.
A. linear
B. nonlinear
C. categorical
D. symmetrical
Which of the following is true about Manhattan distance?
A. it can be used for continuous variables
B. it can be used for categorical variables
C. it can be used for categorical as well as continuous
D. it can be used for constants
In terms of bias and variance. Which of the following is true when you fit degree 2 polynomial?
A. bias will be high, variance will be high
B. bias will be low, variance will be high
C. bias will be high, variance will be low
D. bias will be low, variance will be low
Supervised learning and unsupervised clustering both require at least one
A. hidden attribute.
B. output attribute.
C. input attribute.
D. categorical attribute.
Even if there are no actual supervisors . . . . . . . . learning is also based on feedback provided by the environment
A. Supervised
B. Reinforcement
C. Unsupervised
D. None of the above
When it is necessary to allow the model to develop a generalization ability and avoid a common problem called . . . . . . . .
A. Overfitting
B. Overlearning
C. Classification
D. Regression
Which of the following option is true about k-NN algorithm?
A. it can be used for classification
B. it can be used for regression
C. it can be used in both classification and regression
D. not useful in ml algorithm
Feature can be used as a
A. binary split
B. predictor
C. both a and b
D. none of the above
The average positive difference between computed and desired outcome values.
A. root mean squared error
B. mean squared error
C. mean absolute error
D. mean positive error
The most general form of distance is
A. manhattan
B. eucledian
C. mean
D. minkowski
scikit-learn offers the class______ which is responsible for filling the holes using a strategy based on the mean, median, or frequency
A. LabelEncoder
B. LabelBinarizer
C. DictVectorizer
D. Imputer
Which of the following function provides unsupervised prediction ?
A. cl_forecastb
B. cl_nowcastc
C. cl_precastd
D. none of the mentioned
In the last decade, many researchers started training bigger and bigger models, built with several different layers that's why this approach is called _____
A. Deep learning
B. Machine learning
C. Reinforcement learning
D. Unsupervised learning
Which of the following is a good test dataset characteristic?
A. large enough to yield meaningful results
B. is representative of the dataset as a whole
C. both A and B
D. none of the above
Features being classified is ___ of each other in Nave Bayes Classifier
A. independent
B. dependent
C. partial dependent
D. none
In syntax of linear model lm (formula,data,..), data refers to ______
A. Matrix
B. Vector
C. Array
D. List
How it's possible to use a different placeholder through the parameter______
A. regression
B. classification
C. random_state
D. missing values
Which of the following is a categorical data?
A. branch of bank
B. expenditure in rupees
C. prize of house
D. weight of a person
Linear Regression is a supervised machine learning algorithm.
A. TRUE
B. FALSE
Which of the following is the difference between stacking and blending?
A. stacking has less stable cv
B. in blending, you create out of fold prediction
C. stacking is simpler than blending
D. none of these
Logistic regression is a . . . . . . . . regression technique that is used to model data having a . . . . . . . . outcome.
A. linear, numeric
B. linear, binary
C. nonlinear, numeric
D. nonlinear, binary
You are given reviews of few Netflix series marked as positive, negative and neutral. Classifying reviews of a new Netflix series is an example of
A. supervised learning
B. unsupervised learning
C. semi supervised learning
D. reinforcement learning
In following type of feature selection method we start with empty feature set
A. forward feature selection
B. backword feature selection
C. both a and b
D. none of the above
Neural Networks are complex . . . . . . . . with many parameters.
A. linear functions
B. nonlinear functions
C. discrete functions
D. exponential functions
If Linear regression model perfectly first i.e., train error is zero, then _____
A. Test error is also always zero
B. Test error is non zero
C. Couldn't comment on Test error
D. Test error is equal to Train error
When the number of classes is large Gini index is not a good choice.
A. TRUE
B. FALSE
Data used to build a data mining model.
A. training data
B. validation data
C. test data
D. hidden data
This technique associates a conditional probability value with each data instance.
A. linear regression
B. logistic regression
C. simple regression
D. multiple linear regression
What would you do in PCA to get the same projection as SVD?
A. transform data to zero mean
B. transform data to zero median
C. not possible
D. none of these
The . . . . . . . . of the hyperplane depends upon the number of features.
A. dimension
B. classification
C. reduction
D. none of the above
What is the approach of basic algorithm for decision tree induction?
A. greedy
B. top down
C. procedural
D. step by step
Can we extract knowledge without apply feature selection
A. Yes
B. No
Computers are best at learning
A. facts.
B. concepts.
C. procedures.
D. principles.
KDD represents extraction of
A. data
B. knowledge
C. rules
D. model
Linear Regression is a _____ machine learning algorithm.
A. supervised
B. unsupervised
C. semi-supervised
D. cant say
Which of the following can only be used when training data are linearly separable?
A. linear hard-margin svm
B. linear logistic regression
C. linear soft margin svm
D. the centroid method
The average squared difference between classifier predicted output and actual output.
A. mean squared error
B. root mean squared error
C. mean absolute error
D. mean relative error
Which of the following methods do we use to find the best fit line for data in Linear Regression?
A. Least Square Error
B. Maximum Likelihood
C. Logarithmic Loss
D. Both A and B
Following are the descriptive models
A. clustering
B. classification
C. association rule
D. both a and c
What are common feature selection methods in regression task?
A. correlation coefficient
B. greedy algorithms
C. all above
D. none of these
Which of the following is characteristic of best machine learning method ?
A. fast
B. accuracy
C. scalable
D. all above
Some people are using the term _______ instead of prediction only to avoid the weird idea that machine learning is a sort of modern magic.
A. Inference
B. Interference
C. Accuracy
D. None of above
What characterize unlabelled examples in machine learning:
A. there is no prior knowledge
B. there is no confusing knowledge
C. there is prior knowledge
D. there is plenty of confusing knowledge
What are the different Algorithm techniques in Machine Learning?
A. supervised learning and semi-supervised learning
B. unsupervised learning
C. both A & B
D. none of the mentioned
Which of the following is not Machine Learning?
A. artificial intelligence
B. rule based inference
C. both a and b
D. none of the mentioned
If machine learning model output does not involves target variable then that model is called as
A. descriptive model
B. predictive model
C. reinforcement learning
D. all of the above
Which are two techniques of Machine Learning ?
A. Genetic Programming and Inductive Learning
B. Speech recognition and Regression
C. Both A and B
D. None of the Mentioned
In simple term, machine learning is
A. training based on historical data
B. prediction to answer a query
C. both A and B
D. atomization of complex tasks
Which of the following is the best machine learning method?
A. scalable
B. accuracy
C. fast
D. all of the above
The output of training process in machine learning is
A. machine learning model
B. machine learning algorithm
C. null
D. accuracy
Application of machine learning methods to large databases is called
A. data mining.
B. artificial intelligence
C. big data computing
D. internet of things
If machine learning model output involves target variable then that model is called as
A. descriptive model
B. predictive model
C. reinforcement learning
D. all of the above
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