Decision Trees: Understanding The Construction Principle For Better Predictions

Decision trees are one of the most popular and widely used data mining techniques. They are used to create a model that can be used to make predictions based on data.

Decision trees are based on the concept of a tree structure, where each node represents a decision and each branch represents a possible outcome.

The construction of decision trees starts with selecting the root node, which represents the most important decision or attribute to be made.

The root node is chosen based on the information gain or the reduction in impurity of the data.

The information gain is a measure of how much the impurity of the data decreases after a particular attribute is chosen as the root node. The attribute with the highest information gain is chosen as the root node.

Once the root node is selected, the data is split into subsets based on the values of the root node attribute.

Each subset is then used to form a child node of the root node. The process of selecting the root node and splitting the data into subsets is then repeated for each child node.

This process is repeated until a stopping criterion is met. The stopping criterion can be the maximum depth of the tree, the minimum number of instances in a leaf node, or the minimum impurity in a leaf node.

The decision tree is built in a top-down, recursive manner. The recursion stops when the data is pure, meaning all instances belong to the same class, or when the maximum depth of the tree is reached.

The resulting decision tree can be used to make predictions by traversing the tree from the root node to a leaf node. The class label associated with the leaf node is the predicted class label.

There are different algorithms used to construct decision trees such as ID3, C4.5, and CART. ID3 (Iterative Dichotomiser 3) uses information gain to select the root node, C4.5 uses the information gain ratio and CART (Classification and Regression Tree) uses the Gini Index to select the root node.

One of the main advantages of decision trees is that they are easy to interpret and understand. They are also very versatile, and can be used for both classification and regression problems. Additionally, decision trees can handle both categorical and numerical data.