1. Can logistic regression be used for multi-class classification? A flavour of logistic regression known as OVR (One Vs. Rest) Logistic Regression can be used to solve multi-class classification problems. The algorithm's functioning is crudely explained using an example below: Suppose you have to classify between three classes: Dog, cat and cow. Instead of treating this problem as a multi-class classification problem we can treat it as three independent binary classification problems as following diagram depicts: Now we can simply compare the probability ranking of each of the classes for example for a particular record: DOG Yes = 0.8 CAT Yes = 0.5 COW Yes =0.4 Then the record is a Dog. 2. What is the significance of Gini Index =0? Gini Index = 0 signifies that the dataset entirely consists of only one class. It can also be interpreted that the dataset is entirely pure. 3. What are the differences and similarities between CART and CHAID? 4. Random Forest ...
