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

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?

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?

Q4 | Problem: Players will play if weather is sunny. Is this statement is correct?

Q5 | Multinomial Naïve Bayes Classifier is    _ distribution

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?

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

Q8 | Which of the following is not supervised learning?

Q9 | Gaussian Naïve Bayes Classifier is    _ distribution

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?

Q11 | The cost parameter in the SVM means:

Q12 | We usually use feature normalization before using the Gaussian k

Q13 | The effectiveness of an SVM depends upon:

Q14 | The process of forming general concept definitions from examples of concepts to belearned.

Q15 | Computers are best at learning

Q16 | Data used to build a data mining model.

Q17 | Supervised learning and unsupervised clustering both require at least one

Q18 | Supervised learning differs from unsupervised clustering in that supervised learning requires

Q19 | A regression model in which more than one independent variable is used to predict thedependent variable is called

Q20 | A term used to describe the case when the independent variables in a multiple regression modelare correlated is

Q21 | A multiple regression model has the form: y = 2 + 3x1 + 4x2. As x1 increases by 1 unit (holding x2constant), y will

Q22 | A multiple regression model has

Q23 | A measure of goodness of fit for the estimated regression equation is the

Q24 | The adjusted multiple coefficient of determination accounts for

Q25 | The multiple coefficient of determination is computed by