CS 451 Quiz 5
Logistic Regression
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We need to use a different cost function for logistic regression than for linear regression in order to obtain a convex minimization problem without multiple local minima
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True
False
Logistic regression is an algorithm for
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Regression
Classification
One reason linear regression is not good for classification is that a single training example, even if far from the decision boundary, can adversely influence the results
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True
False
In logistic regression, which equation defines the decision boundary?
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theta' * x = 0
theta' * x = 0.5
theta' * x = 1
For which algorithm is 0 <= h_theta(x) <= 1 always true?
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Linear regression
Logistic regression
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In the logistic regression hypothesis h_theta(x) = g(theta' * x), the function g is
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the Gaussian function
the log function
the sigmoid function
Octave / Matlab provides other optimization algorithms such as Conjugate gradient, BFGS, and LBFGS, but none of them are faster than gradient descent
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True
False
The following is the gradient descent algorithm for ...
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Linear regression
Logistical regression
Both
For multiclass classification with N categories we simply train N binary classifiers and use the one with the maximal hypothesis value
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True
False
The logistic hypothesis h_theta(x) = g(theta' * x) represents
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the probability that the hypothesis is false
the probability that the hypothesis is true
neither
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