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iii. Assertion (A): o adays, predictive models are not able to better predict rare events such as disease or system
failure.
Reason (R): oday's high performance database analytics enable data scientists to utilise large datasets that
contain large or even all of the available data.
Ans. d
iv. Assertion (A): e follo problem decomposition methodology that can be applied to real life problems as ell.
Reason (R): eal life problem solving is complicated.
Ans. a
v. Assertion (A): he data scientist revie s the model during development and before deployment to determine
its quality and ensures that it correctly and completely ans ers the business problem.
Reason (R): Data scientist uses statistical significance tests to verify the model's accuracy.
Ans. b
vi. Assertion (A): After the initial data collection, techniques such as descriptive statistics and visualisations are
applied to datasets.
Reason (R): his step is carried out to evaluate the content, quality, and initial insights of the data.
Ans. a
vii. Assertion (A): oss function learns to increase prediction error over time ith the help of some optimisation
ob ective function.
Reason (R): A loss function is used by machines to learn.
Ans. d
viii. Assertion (A): eal orld data can be strange and deceptive.
Reason (R): Data can be gathered from a variety of sources, including overnment ebsites.
Ans. b
i . Assertion (A): he oot ean quare rror ) is a metric for determining ho ell a regression line fits the
data points.
Reason (R): places a higher eight on the errors due to the squaring element of the function.
Ans. c
. Assertion (A): A split technique divides the provided dataset into t o subsets training and test subset.
Reason (R): As a result, the process is frequently referred to as k fold cross validation.
Ans. c
. hich of these is O analytic based on the type of question ample aper,
a. Descriptive b. tatistical Analysis
c. orecasting d. Data evaluation
Ans. d
. hich of the follo ing statements is are I O ample aper,
i) Different transforms of the data used to train the same machine learning model.
ii) Different machine learning models cannot be trained on the same data.
iii) Different configurations for a machine learning model trained on the same data
a. i) b. ii)
c. oth ii) iii) d. oth i) ii)
Ans. b
. If the problem is based on probabilities of an action, then hich analytic approach
can be used ample aper,
a. redictive odel b. rescriptive
c. Diagnostic d. Descriptive
Ans. a
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