How a Decision Tree Learns Patterns in Machine Learning

When we train a machine learning model like a DecisionTreeRegressor,
the model does not “understand” data like humans do.

Instead, it learns by finding mathematical rules that reduce prediction errors.

Step 1: Provide Training Data

Suppose we have house data like this:

In machine learning:

• Features (inputs) → X
• Target (answer/output) → y

Example:

X = home_data[features]
y = home_data['SalePrice']

Step 2: Train the Model

The model is trained using:

iowa_model.fit(X, y)

This means:

Study the relationship between house features and actual prices.

Step 3: The Model Starts Asking Questions

A Decision Tree learns by creating rules.

It searches for questions that best separate cheaper houses from expensive houses.

For example:

Is size < 1500?

If YES:

Likely cheaper houses

If NO:

Likely more expensive houses

Then it keeps splitting again.

Example:

Is number_of_rooms < 4?
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