This tutorial covers how to find the decision boundary in logistic regression classification with two features.
Select data series 'Class1' and data series 'Class2' on left panel [Points].
Select menu item
Training dataset will be divided on two parts based on parameter Z value.
In dialog on step 1 it will assign Z1=0 to points from Class1 and Z2=1 to points from Class2.
We find the decision boundary curve line in form of X,Y polynomials.
On step 2 select checked 'Reularization' and set parameter Lambda value to 20.
Vary polynomial power parameters M and N
to find best fit but to keep simple formula
to minimize percent of incorrect classified items in training and testing datasets.
On step 2 select Class1 color, points in Class2 wll be brushed with inverted color.
Close the dialog.