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This set of Machine Learning (ML) Multiple Choice Questions & Answers (MCQs) focuses on Machine Learning Set 24
Q1 | What is/are true about ridge regression?1. When lambda is 0, model works like linear regression model2. When lambda is 0, model doesn’t work like linear regression model3. When lambda goes to infinity, we get very, very small coefficients approaching 04. When lambda goes to infinity, we get very, very large coefficients approaching infinity
- 1 and 3
- 1 and 4
- 2 and 3
- 2 and 4
Q2 | We have been given a dataset with n records in which we have input attribute as x and output attribute as y. Suppose we use a linear regression method to model this data. To test our linear regressor, we split the data in training set and test set randomly. Now we increase the training set size gradually. As the training set size increases, what do you expect will happen with the mean training error?
- increase
- decrease
- remain constant
- can’t say
Q3 | We have been given a dataset with n records in which we have input attribute as x and output attribute as y. Suppose we use a linear regression method to model this data. To test our linear regressor, we split the data in training set and test set randomly. What do you expect will happen with bias and variance as you increase the size of training data?
- bias increases and variance increases
- bias decreasesand variance increases
- bias decreases and variance decreases
- bias increases and variance decreases
Q4 | Problem: Players will play if weather is sunny. Is this statement is correct?
- true
- false
Q5 | Multinomial Naïve Bayes Classifier is _ distribution
- continuous
- discrete
- binary
Q6 | The minimum time complexity for training an SVM is O(n2). According to this fact, what sizes of datasets are not best suited for SVM’s?
- large datasets
- small datasets
- medium sized datasets
- size does not matter
Q7 | We usually use feature normalization before using the Gaussian kernel in SVM. What is true about feature normalization? 1. We do feature normalization so that new feature will dominate other 2. Some times, feature normalization is not feasible in case of categorical variables3. Feature normalization always helps when we use Gaussian kernel in SVM
- 1
- 1 and 2
- 1 and 3
- 2 and 3
Q8 | Which of the following is not supervised learning?
- pca
- decision tree
- naive bayesian
- linerar regression
Q9 | Gaussian Naïve Bayes Classifier is _ distribution
- continuous
- discrete
- binary
Q10 | If I am using all features of my dataset and I achieve 100% accuracy on my training set, but~70% on validation set, what should I look out for?
- underfitting
- nothing, the model is perfect
- overfitting
Q11 | The cost parameter in the SVM means:
- the number of cross- validations to be made
- the kernel to be used
- the tradeoff between misclassificati on and simplicity of the model
- none of the above
Q12 | We usually use feature normalization before using the Gaussian k
- e 1
- 1 and 2
- 1 and 3
- 2 and 3
Q13 | The effectiveness of an SVM depends upon:
- selection of kernel
- kernel parameters
- soft margin parameter c
- all of the above
Q14 | The process of forming general concept definitions from examples of concepts to belearned.
- deduction
- abduction
- induction
- conjunction
Q15 | Computers are best at learning
- facts.
- concepts.
- procedures.
- principles.
Q16 | Data used to build a data mining model.
- validation data
- training data
- test data
- hidden data
Q17 | Supervised learning and unsupervised clustering both require at least one
- hidden attribute.
- output attribute.
- input attribute.
- categorical attribute.
Q18 | Supervised learning differs from unsupervised clustering in that supervised learning requires
- at least one input attribute.
- input attributes to be categorical.
- at least one output attribute.
- output attributes to be categorical.
Q19 | A regression model in which more than one independent variable is used to predict thedependent variable is called
- a simple linear regression model
- a multiple regression models
- an independent model
- none of the above
Q20 | A term used to describe the case when the independent variables in a multiple regression modelare correlated is
- regression
- correlation
- multicollinearity
- none of the above
Q21 | A multiple regression model has the form: y = 2 + 3x1 + 4x2. As x1 increases by 1 unit (holding x2constant), y will
- increase by 3 units
- decrease by 3 units
- increase by 4 units
- decrease by 4 units
Q22 | A multiple regression model has
- only one independent variable
- more than one dependent variable
- more than one independent variable
- none of the above
Q23 | A measure of goodness of fit for the estimated regression equation is the
- multiple coefficient of determination
- mean square due to error
- mean square due to regression
- none of the above
Q24 | The adjusted multiple coefficient of determination accounts for
- the number of dependent variables in the model
- the number of independent variables in the model
- unusually large predictors
- none of the above
Q25 | The multiple coefficient of determination is computed by
- dividing ssr by sst
- dividing sst by ssr
- dividing sst by sse
- none of the above