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 model is a supervised machine learning algorithm used for classification and regression tasks.

It works by splitting data into branches based on feature values, creating a tree-like structure where:

• Root Node → Represents the starting point (entire dataset).

• Decision Nodes → Points where the data is split based on a condition (e.g., "Is age > 30?").

• Branches → Outcomes of a condition (Yes/No, True/False).

• Leaf Nodes (Terminal Nodes) → Final predictions or outcomes.

How it Works:

1. The algorithm selects a feature and a condition that best separates the data (using measures like Gini impurity, Entropy (Information Gain), or Variance reduction).

2. It keeps splitting recursively, building a tree until a stopping condition is met (like max depth or minimum samples per node).

3. For a new input, the model follows the conditions down the tree until it reaches a leaf node, which gives the prediction.

Example:

For predicting whether a person buys a product:

• Root Node: "Is age > 30?"

  • Yes → "Is income > 50k?" → Predict Buys

  • No → Predict Doesn’t Buy

Advantages:

• Easy to understand and interpret (like flowcharts).

• Handles numerical and categorical data.

• Requires little data preprocessing.

Disadvantages:

• Can overfit if not pruned.

• Sensitive to small changes in data.

• Often less accurate compared to ensemble methods like Random Forest or XGBoost.

👉 Would you like me to also give you a simple visual diagram example of a decision tree for better clarity?

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