Imagine you're trying to decide whether to go for a picnic. You'd probably ask yourself a series of questions: Is it sunny? Is it too windy? Do I have a blanket? Each answer leads you down a different path of consideration. Decision trees in machine learning work in a remarkably similar, intuitive way. They are a powerful and visually understandable method for both classification and regression tasks.
At its core, a decision tree is a flowchart-like structure where each internal node represents a 'test' on an attribute (e.g., 'Is the color red?'). Each branch represents the outcome of the test, and each leaf node (or terminal node) represents a class label (in classification) or a continuous value (in regression).
graph TD;
A[Is it sunny?] -->|Yes| B{Is it windy?};
A -->|No| C[Don't go picnic];
B -->|Yes| D[Don't go picnic];
B -->|No| E[Go picnic];
Let's break down the components. The 'root node' is the starting point, the first question asked. 'Internal nodes' represent subsequent questions or tests. 'Branches' are the possible answers to these tests. Finally, 'leaf nodes' are the conclusions – the final decisions or predictions.
For example, if we're building a decision tree to predict if a customer will buy a product, the root node might be 'Age > 30'. If the answer is 'Yes', we might follow one branch to another question like 'Income > $50,000'. If the answer to the first question was 'No', we'd follow a different branch, perhaps to a leaf node directly predicting 'Will buy' because younger individuals in our dataset tend to buy more frequently.
graph TD;
A[Age > 30?] -->|Yes| B[Income > $50k?];
A -->|No| C[Buy];
B -->|Yes| D[Buy];
B -->|No| E[Don't Buy];
The 'best' way to split the data at each node is determined by algorithms that aim to maximize information gain or minimize impurity. Popular metrics for this include Gini impurity and entropy. In essence, the tree learns which questions are most effective at separating the data into distinct outcomes. This process of building the tree is called 'training'.
Once a decision tree is trained, making a prediction for a new data point is as simple as traversing the tree from the root node, following the branches based on the data point's attributes, until you reach a leaf node. That leaf node's value is your prediction.