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date: 2026-03-10

Decision Tree Regression

This project demonstrates Decision Tree Regression using Python and Scikit-learn.

Decision Tree Regression is a non-linear model that splits the data into smaller parts using conditions and predicts values based on those splits.

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📌 What is Decision Tree Regression?

Decision Tree Regression is a machine learning algorithm that works like a tree structure.

It divides the dataset using rules (if-else conditions) and makes predictions at the leaf nodes.

Unlike Linear Regression, Decision Tree can handle non-linear relationships.

Example logic: - if value < 5 → predict 10 - else → predict 50

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📊 Dataset

Simple numeric dataset used for practice. X = [1,2,3,4,5,6,7,8,9,10] y = [1,4,9,16,25,36,49,64,81,100]

This dataset represents a non-linear pattern.

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⚙️ Libraries Used

• numpy
• matplotlib
• scikit-learn

Install using:

pip install numpy matplotlib scikit-learn

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🧠 Model Used

DecisionTreeRegressor()

Decision Tree splits data into smaller regions and predicts values.

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💻 Code

import numpy as np
import matplotlib.pyplot as plt

from sklearn.tree import DecisionTreeRegressor
from sklearn.metrics import r2_score

X = np.array([1,2,3,4,5,6,7,8,9,10]).reshape(-1,1)
y = np.array([1,4,9,16,25,36,49,64,81,100])

model = DecisionTreeRegressor()

model.fit(X,y)

y_pred = model.predict(X)

print("R2 score:", r2_score(y,y_pred))

plt.scatter(X,y)
plt.plot(X,y_pred)

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📈 Output

R2 Score: 1.0 Decision Tree fits training data perfectly, but it may cause overfitting.

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⚠️ Important Concepts

Non-linear model

Works like if-else

Can overfit

Tree depth controls complexity

Base model for Random Forest

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👨‍💻 Author

Suraj Singh Machine Learning Practice Series