The advantage of using a model-based approach is that is more closely tied to the model performance.(a) True(b) FalseI got this question by my school principal while I was bunking the class.This interesting question is from caret topic in division Machine Learning of Data Science

The advantage of using a model-based approach is that is more closely

1.	The advantage of using a model-based approach is that is more closely tied to the model performance.(a) True(b) FalseI got this question by my school principal while I was bunking the class.This interesting question is from caret topic in division Machine Learning of Data Science
Answer» Right choice is (a) True To EXPLAIN I WOULD say: Model-based APPROACH is able to INCORPORATE the correlation structure between the predictors into the IMPORTANCE calculation.

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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)?

The advantage of using a model-based approach is that is more closely tied to the model performance.(a) True(b) FalseI got this question by my school principal while I was bunking the class.This interesting question is from caret topic in division Machine Learning of Data Science

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