# Classification & Regression Trees (CART)

### What is a Decision Tree?

Decision Tree is one of the predicting modeling techniques that can be used for both Regression and Classification problems.

### Explain the difference between Gini, Entropy, and Information Gain

Gini Score and Entropy are both measures that quantify the impurity of a node in a decision tree

### How does a decision tree create splits from continuous features?

The continuous variable is first sorted in ascending order of its values, and the midpoint between each pair of adjacent observations is calculated.

### How does pruning a tree work?

Pruning refers to the process of simplifying a decision tree after it has already been created by removing leaf nodes that result in the smallest information gain.

### What is CART?

CART, or Classification and Regression Trees, simply refers to the standard algorithm for creating a decision tree.

### What are the advantages and disadvantages of using a Decision Tree?

Advantages: Simple and highly intuitive interpretation

### What is Bagging?

Bagging, or “Bootstrap Aggregation”, refers to an ensemble design structure in which each instance of the ensemble, such as an individual decision tree in a Random Forest, is created on a different subset of the original dataset.

### What is Random Forest?

Random Forest is a supervised machine learning algorithm, which can be used for solving both regression and classification problems, including both binary and multi-class classification.

### What parameters can be tweaked for a Random Forest model? Explain in detail

There are three key parameters that can be tweaked: (a) Number of trees, (b) Number of Features and, (c) Sub sample size