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Can logistic regression be used for non linear classification?
Logistic regression is known and used as a linear class i fier. It is used to come up with a hyper plane in feature space to separate observations that belong to a class from all the other observations that do not belong to that class.
Is the logistic function linear?
Logistic regression is considered as a linear model because the decision boundary it generates is linear, which can be used for classification purposes.
When is logistic regression used as a linear classifier?
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… Logistic regression is known and used as a linear classifier.
What are feature selection methods to implement for logistic regression?
I tried several ways of selecting predictors for a logistic regression in R. I used lasso logistic regression to get rid of irrelevant features, cutting their number from 60 to 24, then I used those 24 variables in my stepAIC logistic regression, after which I further cut 1 variable with p-value of approximately 0.1.
When to use a nonlinear decision boundary in logistic regression?
The linear decision boundary is used for reasons of simplicity following the Zen mantra – when in doubt simplify. In those cases where we suspect the decision boundary to be nonlinear, it may make sense to formulate logistic regression with a nonlinear model and evaluate how much better we can do. That is what this post is about.
Which is logisticregression model handles L2 or no penalty?
‘newton-cg’, ‘lbfgs’, ‘sag’ and ‘saga’ handle L2 or no penalty ‘liblinear’ and ‘saga’ also handle L1 penalty ‘saga’ also supports ‘elasticnet’ penalty