ScholarMatic: Explanation & Answer
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1.Logistic regression differs from linear regression in this fundamental way:
logistic regression is designed to predict nominal classes
in logistic regression, the logs of the predictor attributes are used
logistic regression can handle more predictor attributes
logistic regression is generally more accurate than linear regression
2.It is usual to normalize all attribute values prior to using the Euclidean distance formula.
(a)___________ attributes are normalized to lie between 0 and 1.
(b) for ________ attributes, if the values are identical they get a value of 0, else they get a value of 1
(a) numeric (b) nominal
(a) nominal (b) numeric
(a) positive (b) negative
(a) nominal (b) string
3.What’s linear about linear regression?
Y is the attribute to be predicted.
X is the attribute used to predict Y.
the relationship between X and Y is assumed to be adequately repesented by a straight line.
the relationship between X and Y is assumed to be adequately repesented by a line.
the X predictor attributes are lined up in an equation
ScholarMatic: Explanation & Answer
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