What’s a decision tree model?
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Supervised and Unsupervised Learning are two primary types of machine learning, differing mainly in The primary goal of a data science project is to extract actionable insights from data to support better decision-making, predictions, or automation—ultimately solving a specific business or real-world problem.
A decision tree is a type of machine learning model used for classification and regression tasks. It works like a flowchart, where data is split into branches based on decision rules until an outcome (prediction) is reached.
How It Works
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Root Node → The starting point that represents the entire dataset.
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Decision Nodes → Points where data is split based on a condition (e.g., Is age > 30?).
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Branches → Paths that represent the outcome of a decision.
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Leaf Nodes → The final outcomes (predicted class or value).
Example
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If building a tree to decide whether to approve a loan:
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Root: Applicant’s credit score
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Decision: If score > 700 → Approve; Else check income.
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Leaf: Approved or Rejected.
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Advantages
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Easy to understand and interpret (like human decision-making).
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Handles both numerical and categorical data.
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Requires little data preprocessing.
Disadvantages
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Can overfit if the tree is too deep.
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Small changes in data may drastically change the tree.
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Less accurate compared to ensemble models (like Random Forests).
✅ In simple terms: A decision tree is a tree-shaped model that makes predictions by asking a series of yes/no questions, leading to a decision at the end.
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