How does a decision tree algorithm work in data science?

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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.

How a Decision Tree Works:

  1. Start with the entire dataset.

  2. Choose the best feature to split the data based on some criterion (like Gini Impurity, Entropy/Information Gain, or Mean Squared Error for regression).

  3. Split the dataset into branches based on the selected feature.

  4. Repeat the process recursively for each branch (sub-dataset), creating smaller and smaller subsets.

  5. Stop when:

    • All the data in a node belongs to the same class (for classification).

    • A maximum depth is reached.

    • There are too few samples left to split further.

 A Simple Example (Classification):

Let's say you're trying to classify whether someone will buy a sports car:

  1. Root question: Is the person under 30?

    • Yes ➡️ Ask: Do they have a high income?

      • Yes ➡️ Predict: Buy a sports car ✅

      • No ➡️ Predict: Don’t buy ❌

    • No ➡️ Ask: Are they married?

      • Yes ➡️ Predict: Don’t buy ❌

      • No ➡️ Predict: Might buy 🤷

Each question splits the data and helps make a more accurate prediction.

 Key Concepts:

  • Node: A point where a decision is made.

  • Root Node: The topmost decision node.

  • Leaf Node: Final outcome/prediction.

  • Branch: A possible outcome from a node.

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