Logistic Regression
29 January 2024 -
less than 1 min read time
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Data Science
Machine Learning
Logistic Regression
- Does not have a closed form solution to directly calculate the loss function.
- Since the loss function is convex so gradient descent can be used to minimize the cost.
- Decision boundaries can be drawn between two classes to distinguish positive and negative.
- Predicts a probability and if it is greater than 0.5 then its positive, othewise negative.
Softmax Regression
- Same principle as logistic regression except that it is for multi class classification.
- normalize with sum of all exponentials.
- Cannot be used for multi output classification.
- Uses cross-entropy loss as cost function.