Decision trees are powerful classifiers. They use a tree-like structure. This structure makes decisions. Building one from scratch clarifies concepts. It reveals the inner workings. We will explore core components. Then we’ll outline the building process.
A decision tree has nodes and leaves. Internal nodes represent features. Branches represent feature values. Leaf nodes represent predictions. The tree splits data recursively. Each split is based on a feature. The goal is to create pure leaves. Pure leaves contain mostly one class.
To build a tree, we need data. Labeled data is required for training. We also need an impurity measure. Gini impurity or entropy are common. Impurity measures node homogeneity. Lower impurity is better. A pure node has zero impurity.
Gini impurity measures class distribution. For a node, calculate class proportions. Square each proportion. Sum these squared proportions. Subtract this sum from one. This gives the Gini impurity. Lower Gini means purer node.
The algorithm is recursive. Start with the root node. This node contains all data. For each node, find the best split. Iterate through features. For each feature, consider possible splits. Calculate impurity after each split. Choose the split with lowest impurity.
Splitting involves selecting a feature. It also means choosing a threshold. For numerical features, try different thresholds. For categorical, consider subsets. Evaluate impurity reduction for each split. Information gain or Gini gain is used. Gain is impurity before minus after split. Maximize this gain for best split.
After finding the best split, create child nodes. Data is divided based on split condition. Repeat the splitting process for each child. This continues recursively. When to stop splitting? Stopping criteria are necessary.
Stopping conditions include max depth. Limit tree depth to prevent overfitting. Minimum samples per leaf is another. Stop splitting if node samples are few. If a node is already pure, stop. Zero impurity means no further split needed.
Building a decision tree involves recursion. It needs impurity calculation. It requires finding best splits. Stopping criteria are essential too. Understanding these steps is crucial. Building from scratch provides deep insight. It helps appreciate library implementations.