Decision Tree Regression

1. What is Decision Tree Regression?

Imagine we want to predict something with numbers (like the price of a house or the temperature tomorrow), and we don’t want a straight line like in Linear Regression.
Instead, we want to split our problem into smaller questions, like a flowchart — that’s Decision Tree Regression.

Core Concept (Simplified)

Think of a tree:

  • Each branch asks a question (like: “Is the house area > 1500 sq ft?”)
  • Depending on the answer (yes/no), it moves to the next branch.
  • At the leaf, we get a number — the predicted value.

It divides the data into pieces, and in each piece, the prediction is just the average of the numbers in that group.

2. How It Works (Step-by-Step in Easy Terms)

  1. Start with all data.
  2. Find the best feature to split — the one that separates values most cleanly.
  3. Split the dataset into two groups based on a rule (e.g., “if Age < 30”).
  4. Repeat for each group — until:
    • A minimum number of samples is left, or
    • Further splitting doesn’t help much.
  5. At the end, each path leads to a number — our predicted value.

3. Example (Text Story)

Let’s say we want to predict the rent of an apartment.

We ask:

  • Is the size > 1000 sq ft?
    • YES → Is it furnished?
      • YES → Rent = ₹25,000
      • NO → Rent = ₹20,000
    • NO → Is it in city center?
      • YES → Rent = ₹18,000
      • NO → Rent = ₹12,000

This is a decision tree that splits conditions, then assigns a prediction.

Real-Life Use Case 1: Predicting House Prices

A real estate company uses Decision Tree Regression to predict house prices. It asks:

  • Is the area > 1500 sq ft?
  • Is the location urban?
  • Does it have a swimming pool?

Each answer helps narrow down the price range. The final prediction is the average price of houses matching those conditions.

Real-Life Use Case 2: Predicting Crop Yield for Farmers

A farming app predicts crop yield based on:

  • Soil pH level
  • Rainfall
  • Temperature
  • Fertilizer used

The decision tree splits:

  • Is rainfall > 500 mm?
  • Is pH < 6.5?

Eventually, each leaf shows the predicted yield in kg/acre.

Decision Tree Regression – Decision Tree example with Simple Python