A researcher is interested in how variables, such as GRE (Graduate Record Exam scores), GPA (grade point average) and prestige of the undergraduate institution, effect admission into graduate…

The correct answer is B. Logistic Regression. Logistic regression Pros: Computationally inexpensive, easy to implement, knowledge representation easy to Cons: Prone to underfitting, may have low accuracy Works with: Numeric values, nominal values

Supervised Learning

Question

A researcher is interested in how variables, such as GRE (Graduate Record Exam scores), GPA (grade point average) and prestige of the undergraduate institution, effect admission into graduate school. The response variable, admit/don't admit, is a binary variable. Above is an example of

Options

ALinear Regression
BLogistic Regression
CRecommendation system
DMaximum likelihood estimation
EHierarchical linear models

How the community answered

(49 responses)

A
12% (6)
B
80% (39)
C
4% (2)
D
2% (1)
E
2% (1)

Explanation

Logistic regression Pros: Computationally inexpensive, easy to implement, knowledge representation easy to Cons: Prone to underfitting, may have low accuracy Works with: Numeric values, nominal values

Topics

#logistic regression#binary classification#supervised learning#classification

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