What’s a decision tree model?

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Supervised and Unsupervised Learning are two primary types of machine learning, differing mainly in how they process and learn from data.

A decision tree model is a type of supervised machine learning algorithm used for both classification and regression tasks. It works by splitting data into branches based on decision rules, leading to a tree-like structure where each internal node represents a decision on a feature, each branch represents an outcome of the decision, and each leaf node represents a final prediction or outcome.

 How It Works (Simplified)

1. Start at the root node (the top of the tree).

2. Pick the best feature to split the data based on a metric (like Gini impurity, entropy, or variance).

3. Split the dataset into subsets according to the feature’s values.

4. Repeat the process for each child node until:

   • A stopping condition is met (e.g., max depth, minimum samples, pure class).

   • A leaf node is reached.

Key Terms

• Root Node: The first decision in the tree.

• Internal Nodes: Feature-based decisions.

• Leaf Nodes: Final output (class label or predicted value).

• Depth: Number of splits from root to a leaf.

 Pros

• Easy to understand and visualize.

• Works with both numerical and categorical data.

• No need for feature scaling.

• Can handle non-linear relationships.

Cons

• Can overfit the data (especially deep trees).

• Not as accurate as other models in some cases.

• Small changes in data can produce a different tree (high variance).

Common Uses

• Customer churn prediction

• Medical diagnosis

• Risk assessment

• Loan approval

For better performance, decision trees are often used in ensemble methods like Random Forests or Gradient Boosted Trees.

Read More

What does unsupervised learning do?

What is the purpose of feature engineering?

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