Point out the correct statement.(a) findLinearColumns will also return a vector of column positions can be removed to eliminate the linear dependencies(b) findLinearCombos will return a list that enumerates dependencies(c) the function findLinearRows can be used to generate a complete set of row variables from one factor(d) none of the mentionedThis question was posed to me in an interview for job.Question is from caret in division Machine Learning of Data Science

Point out the correct statement.(a) findLinearColumns will also return

1.	Point out the correct statement.(a) findLinearColumns will also return a vector of column positions can be removed to eliminate the linear dependencies(b) findLinearCombos will return a list that enumerates dependencies(c) the function findLinearRows can be used to generate a complete set of row variables from one factor(d) none of the mentionedThis question was posed to me in an interview for job.Question is from caret in division Machine Learning of Data Science
Answer» Correct option is (b) findLinearCombos will return a list that ENUMERATES DEPENDENCIES To explain: For each linear combination, it will incrementally REMOVE columns from the matrix and test to SEE if the dependencies have been resolved.

Discussion

No Comment Found

Related InterviewSolutions

We have below data with 10 transactions. What is the “Lift Ratio” for “if white then blue”?
Transactions#FaceplateColorsPurchased1redwhitegreen2whiteorange3whiteblue4redwhiteorange5redblue6whiteblue7whiteorange8redwhitebluegreen9redwhiteblue10yellow
We have below data with 10 transactions. What is the performance measure “Confidence” for “if white then blue”?
Transaction#FaceplateColorsPurchased1redwhitegreen2whiteorange3whiteblue4redwhiteorange5redblue6whiteblue7whiteorange8redwhitebluegreen9redwhiteblue10yellow
We have below data with 10 transactions. What is the performance measure “Support” for “if white then blue”?
UnivSATTop10AcceptSFRatioExpensesGradRateBrown131089221322,70494CalTech141510025663,57581CMU12606259925,02672Columbia131076241231,51088Comell128083331321,86490Dartmouth134089231032,16295Duke131590301231,58595Georgetown125574241220,12692Harvard140091141139,52597JohnHopkins13057544758,69187MIT138094301034,87091Northwestern126085391128,05289NotreDame125581421315,12294PennState108138541810,18580Priceton13759114830,22095Purdue10052890199,06669Stanford136090201236,45093TexasA&M10754967258,70467UCBerkeley124095401715,14078UChicago129075501338,38087UMichigan118065681615,47085UPenn128580361127,55390UVA122577441413,34992UWisconsin108540691511,85771Yale137595191143,51496
SATAverageSAT score of new freshmenTop10% new freshmen in top 10% of highschool classAccept% of applicants accepted SFRatioStudent to faculty ratioSExpensesEstimated annual expensesGradRateGraduation Rate(%)
Consider universities dataset below. Data for 25 undergraduate programs at business schools in US universities in 1995. The dataset excludes image variables (student satisfaction, employer satisfaction, dean’s opinions, etc.). Given this
We have trained/executed our model with the given dataset. We have noticed that we have used a regression model and it is suffering from multicollinearity. Is it possible to improvise on our model without losing any information?
When is Ridge regression used and when is Lasso regression (ideally)?

Discussion

No Comment Found

Related InterviewSolutions

Reply to Comment