Decision trees concept

Properties of decision tree

• Root node (parent node)
• Internal node (child node)
• Leaf node (terminal node)
• Test condition

Example

flowchart TD
    A[Root node]
    A -->B[Internal Node]
    A -->C[Internal Node]
    D[Leaf Node]

Tree induction

Greedy strategy

Split the records based on an attribute test that optimizes certain criterion. Nodes with homogeneous class distribution (low degree of impurity) are preferred. Need a measure of node impurity.

Entropy:

• Measure homogeneity of a node
• Maximum when records are equally distributed among all classes implying the least information.
• Minimum when all records belong to one class (of target attribute), implying most information.

Notes

Do not use ids for data modeling and prediction. The higher the entropy/gini/misclassification the lower the purity of a node. Error value should be less.

Detailed explanation

Think of a decision tree like a flowchart that helps make decisions through a series of questions. It’s similar to how a doctor diagnoses a patient - starting with general questions and getting more specific until reaching a conclusion.

As its heart, a decision tree start with a single point (called the root node) that represents your entire dataset. From there, it asks a series of yes/no questions about different features in your data. Each question splits the data into smaller groups based on the answers. For example, if you are trying to predict whether someone will like a movie, the tree might first ask

Is the movie longer than 2 hours?

Then based on that answer, it might asks

Is it an action movie

and so on.

What makes decision trees particularly useful is how they choose which questions to ask. They use mathematical measures like entropy or gini index to determine which splits will be informative. Think of it like playing 20 questions - you want each question to give you the most useful information possible. The tree automatically finds the questions that best separate the data into distinct groups.

One of the most appealing aspects of decision trees is their transparency. Unlike many other machine learning methods that work like a black box, decision trees shows you exactly how they reached their conclusion. You can follow the path from top to bottom and understand how they reached their conclusion. You can follow the path from top to bottom and understand each decision point along the way. This makes them especially valuable in fields like medicine or finance where you need to explain why a particular decision was made.

However, decision trees do have their challenger. They can sometimes become too complex, like a person who asks too many questions before making a decision. This is called overfitting, and it happens when the tree starts paying attention to noise in the training data rather than finding genuine patterns. To address this, we often need to prune the tree - essentially simplifying it by removing some of the less important decision points.

Another interesting aspects is how decision trees handle different types of data. They work equally well with numerical data (like age or income) and categorical data (like color or type). They can even handle missing data reasonably well, which is quite valuable in real - world applications where data if often incomplete.

The process of building a tree naturally reveals which feature are most important for making predictions. The questions asked at the top of the tree usually the most crucial ones, while those near the bottom have less impact on the final decision. This insight into feature importance can be incredibly valuable for understanding.