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Is the decision boundary for logistic regression linear?
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. The decision boundary is thus linear .
Why is Logistic regression decision boundary linear?
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.)
Can Logistic regression solve non linear problems?
So to answer your question, Logistic regression is indeed non linear in terms of Odds and Probability, however it is linear in terms of Log Odds.
Why logistic regression has 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.) of its parameters!
How is the decision boundary used in logistic regression?
Our intention in logistic regression would be to decide on a proper fit to the decision boundary so that we will be able to predict which class a new feature set might correspond to. The interesting fact about logistic regression is the utilization of the sigmoid function as the target class estimator.
Which is the dashed line in logistic regression?
In the above diagram, the dashed line can be identified a s the decision boundary since we will observe instances of a different class on each side of the boundary. Our intention in logistic regression would be to decide on a proper fit to the decision boundary so that we will be able to predict which class a new feature set might correspond to.
How is logistic regression used in binary classification?
The fundamental application of logistic regression is to determine a decision boundary for a binary classification problem. Although the baseline is to identify a binary decision boundary, the approach can be very well applied for scenarios with multiple classification classes or multi-class classification.
Which is an example of a logistic regression?
Logistic regression is a method for classifying data into discrete outcomes. For example, we might use logistic regression to classify an email as spam or not spam. In this module, we introduce the notion of classification, the cost function for logistic regression, and the application of logistic regression to multi-class classification.