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Logistic Regression
| Name |
Logistic Regression |
| Description |
The method of logistic regression addresses the task of concept
learning. A linear model of the following form is constructed:
Y = b0 + b1* X1 +
b2* X2 + ... + bk *
Xk ,
where Y is the logit tranformation of the probability p.
The logit transformation of the probability of
a value is defined as
Y = log (p / (1 - p)) ,
where p is the probability of an outcome.
The linear function can also be written as a
prediction of the probability of a value, e.g.
P(class = pos) = 1 / (1 + ea + b1 *
X1 + b2 * X2 ... +
bk * Xk)
The constant a and the weights b1 .. bn are
chosen by a regression method so that the predictions for the class
are optimal for a given set of classified examples.
A number of tools are available for computing the weights. |
| Example Languages |
Numerical Values
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| Dm Step |
Concept Learning
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| Method Type |
Method
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