Which of the following shows correct relative order of importance?(a) question->features->data->algorithms(b) question->data->features->algorithms(c) algorithms->data->features->question(d) none of the mentionedI have been asked this question in unit test.I want to ask this question from Prediction Motivation topic in division Machine Learning of Data Science

Which of the following shows correct relative order of importance?(a)

1.	Which of the following shows correct relative order of importance?(a) question->features->data->algorithms(b) question->data->features->algorithms(c) algorithms->data->features->question(d) none of the mentionedI have been asked this question in unit test.I want to ask this question from Prediction Motivation topic in division Machine Learning of Data Science
Answer» Correct option is (B) QUESTION->data->FEATURES->algorithms Explanation: Garbage in should be equal to garbage out.

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