| layout | title | subtile | comments |
|---|---|---|---|
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Infamous Logistic Regression |
Logistic Regression |
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What exactly is logistic regression. For binary classification, logistic regression computes the posterior probability of class C1 as a logistic sigmoid acting on a linear function of the feature vector x: p(C1|x)=y(x)=σ(wTx).
σ (.) is the logistic sigmoid function. σ(a)=1/(1+exp(-a)) + d(σ)/d(a)=σ(1-σ).
Now the goal is to maximize the conditional likelihood p(t/w).
- we take the log to work with the equation and minimize the negative term.
- To find the minimum of the error, we use simple gradient descent.