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Is the decision boundary in logistic regression linear?
Logistic Regression has traditionally been used as a linear classifier, i.e. when the classes can be separated in the feature space by linear boundaries. That can be remedied however if we happen to have a better idea as to the shape of the decision boundary… The decision boundary is thus linear .
Can logistic regression learn non linear decision boundary?
Logistic regression has traditionally been used to come up with a hyperplane that separates the feature space into classes. But if we suspect that the decision boundary is nonlinear we may get better results by attempting some nonlinear functional forms for the logit function.
Why does logistic regression have a linear decision boundary?
The short answer is: Logistic regression is considered a generalized linear model because the outcome always depends on the sum of the inputs and parameters. Or in other words, the output cannot depend on the product (or quotient, etc.)
What is the decision boundary for logistic regression?
The decision boundary f (x,y; c) = 0 in feature space, separates the observations that belong to class A from those that do not belong to class A. c are the parameters to be estimated. Consider a decision boundary that can be expressed as a polynomial in feature variables but linear in the weights/coefficients.
What do you need to know about logistic regression?
In summary, these are the three fundamental concepts that you should remember next time you are using, or implementing, a logistic regression classifier: 1. Logistic regression hypothesis 2. Logistic regression decision boundary 3. Logistic regression cost function
Can a decision boundary be a non-linear function?
Logistic regression decision boundaries can also be non-linear functions, such as higher degree polynomials. The scikit-learn library does a great job of abstracting the computation of the logistic regression parameter θ, and the way it is done is by solving an optimization problem.
How is logistic regression used as a nonlinear classifier?
Logistic regression has traditionally been used to come up with a hyperplane that separates the feature space into classes. But if we suspect that the decision boundary is nonlinear we may get better results by attempting some nonlinear functional forms for the logit function.