Hello,
I have some questions about linear classification and linear regression. This week's material covers the model for linear classification which consists of going from an input vector to a discrete value K, being the class. In this way we obtain linear decision boundaries.
If I correctly understood, the process to find the decision boundary involves the Logistic regression algorithm. But what is the relation to the model for linear regression? First, is it right that the model for linear regression is about "estimating" some value for a continuous variable based on an input vector? If that so, is it correct to consider the model for linear classification as based on the model for regression? (i.e: we decide upon the boundaries for different classes based on the values obtained with the regression algorithm?)
Thank you for your help,
Emile
