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. In e run our modelling process on different subsets of the data to get multiple measures of
model quality.
a. rain est plit b. ross alidation
c. achine earning d. alidation
Ans. b. ross alidation
. he data scientist ill use a for predictive modelling.
a. Algorithm b. raining set
c. Data compilation d. Data preparation
Ans. b. raining set
. Assertion (A): he rain est procedure is appropriate hen there is a sufficiently large dataset available.
Reason (R): he larger the dataset, the more accurate is the prediction.
a. oth Assertion A) and eason ) are true and eason ) is the correct e planation of Assertion A).
b. oth Assertion A) and eason ) are true, but eason ) is not the correct e planation
of Assertion A).
c. Assertion A) is true, but eason ) is false.
d. Assertion A) is false, but eason ) is true.
Ans. a. oth Assertion A) and eason ) are true and eason ) is the correct e planation of Assertion A)
. egression functions predict a , and classification predicts a label.
a. output b. quantity
c. loss d. logic
Ans. b. quantity
. hich of the follo ing is a correct formula for calculating
a. redicted i Actual i) b. redicted i Actual i)
i i
c. Actual i redicted i) d. Actual i redicted i)
i i
Ans. a. redicted i Actual i)
i
. onsider the follo ing data. Identify hich of the follo ing commands are correct to use split )
x Y M onth d ay F F M C D M C D C I SI RH w ind r ain ar ea
1 7 8 2 5 sep w ed 9 0 .1 8 2 .9 7 3 5 .7 6 .2 4 5 2 .2 0 .0 4 .8 8
3 5 6 3 sep tu e 9 0 .3 8 0 .7 7 3 0 .2 6 .3 6 2 4 .5 0 .0 0 .0 0
7 5 9 9 f eb thu 8 4 .2 6 .8 2 6 .6 7 .7 7 9 3 .1 0 .0 0 .0 0
4 9 1 4 4 au g thu 9 5 .8 1 5 2 .0 6 2 4 .1 1 3 .8 2 1 4 .5 0 .0 0 .0 0
4 6 4 6 4 f eb tu e 7 5 .1 4 .4 1 6 .2 1 .9 7 7 5 .4 0 .0 2 .1 4
a. train, test,y train,y test train test split ,y,test si e . )
b. train, test,y train,y test train test split ,y,test si e . , ,y)
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