DDE-1/Classification.md

 ## Classification Problem
 
-In the previous week, we discuss one of the predictive learning tasks, [regression](DDE-1/Regression), we the goal is to estimate the target value from the input array X by using alternative mathematical models. The objective in classification is similar; we use the input feature matrix X to predict a class, a set of discrete values. Herein, the mathematical model divides the feature space (X) into regions, which are separated by the decision boundaries. The learning procedure is, therefore, focused on identifying these hyperplanes. 
+In the previous week, we discuss one of the predictive learning tasks, [regression](DDE-1/Regression), we the goal is to estimate the target value from the input array X by using alternative mathematical models. The objective in classification is similar; we use the input feature matrix X to predict a class, a set of discrete values. Herein, the mathematical model divides the feature space (X) into regions, which are separated by the decision boundaries. The learning procedure is, therefore, focused on identifying these hyperplanes. In the learning process, we can follow a probabilistic approach and create a generative model to sample from the learnt probability space (Bayes' theorem). This is referred as "probabilistic generative models". Alternatively, we can fit a model to give a probability to belong a class given X (y(x) = probability). This is called discriminative approach. Logistic regression is a good example of this strategy.   
 
 ## Linear Classifiers: Logistic regression