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What is the decision boundary of logistic regression?
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.
Can logistic regression classify non linear decision boundary?
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…
What is a decision boundary in a pattern classification problem?
In a statistical-classification problem with two classes, a decision boundary or decision surface is a hypersurface that partitions the underlying vector space into two sets, one for each class. A decision boundary is the region of a problem space in which the output label of a classifier is ambiguous.
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.
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.
How is a logistic regression model trained on?
The model is trained on a set of provided example feature vectors, x ( i), and their classifications, y ( i) = 0 or 1, by finding the set of parameters that minimize the difference between ˆy ( i) and y ( i) in some sense.
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.