🌳 Cracking Decision Trees: A Visual Guide to Classification in Python

“A Decision Tree is not just a model — it’s a flowchart of logic and learning.”

— Tilak Savani

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🧠 Introduction

Decision Trees are one of the most intuitive and explainable machine learning algorithms. They mimic human decision-making and are used for both classification and regression tasks.

In this blog, we’ll break down how they work — from logic to math — and implement one using scikit-learn.

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🤔 What Is a Decision Tree?

A Decision Tree splits your dataset into smaller subsets based on feature values, forming a tree-like structure.
Each internal node asks a question, and the branches represent the answers.

Example:

          [Age > 25?]
           /     \
         Yes     No
        /         \
   [Income > 50K?]  Reject

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⚙️ How It Works (Step-by-Step)

1. Start with all the data.

2. For each feature, calculate how "pure" the split would be.

3. Choose the best feature to split on (highest information gain or lowest Gini).

4. Repeat the process on each subset recursively until stopping criteria is met.

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🧮 Math Behind Decision Trees

Decision Trees use criteria like Gini Impurity or Information Gain (Entropy) to decide the best split.

✳️ 1. Gini Impurity

Used in sklearn by default. Measures how "mixed" the labels are in a node.

    Gini = 1 − Σ(pᵢ²)

Where:

• pᵢ = probability of class i in the node

A pure node (all same class) has Gini = 0.

🔍 2. Entropy & Information Gain

Entropy quantifies disorder:

    Entropy = − Σ(pᵢ * log₂(pᵢ))

Information Gain is the reduction in entropy after a split:

    Gain = Entropy(parent) − [Weighted avg. Entropy(children)]

Decision Trees using entropy try to maximize gain at each node.

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🧪 Python Code: Classification Example

Let’s build a tree to classify whether a person buys a product based on age and salary.

import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree
import matplotlib.pyplot as plt

# Sample Data
data = {
    'Age': [22, 25, 47, 52, 46, 56],
    'Salary': [15000, 29000, 48000, 60000, 52000, 61000],
    'Buys': [0, 0, 1, 1, 1, 1]
}

df = pd.DataFrame(data)

X = df[['Age', 'Salary']]
y = df['Buys']

# Train Model
clf = DecisionTreeClassifier(criterion='entropy', max_depth=3)
clf.fit(X, y)

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📊 Visualize the Tree

plt.figure(figsize=(10, 6))
tree.plot_tree(clf, filled=True, feature_names=['Age', 'Salary'], class_names=['No', 'Yes'])
plt.title("Decision Tree for Product Purchase")
plt.show()

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🧪 Predict

# Predict for someone aged 30 with 40K salary
print(clf.predict([[30, 40000]]))  # Output: [0] (Not likely to buy)

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🌍 Real-World Applications

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✅ Advantages

• Easy to understand and interpret

• No need for feature scaling

• Works with both numerical and categorical data

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⚠️ Limitations

• Prone to overfitting (can be solved by pruning or using ensembles like Random Forest)

• Not as accurate as other models for complex datasets

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🧩 Final Thoughts

Decision Trees are a powerful blend of logic, math, and machine learning. They're transparent, fast, and form the building block of ensemble models like Random Forest and Gradient Boosting.

Whether you're a beginner or building AI at scale, decision trees are a must-know algorithm.

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