Classification Algorithms

Categorize data with AI.

What is Classification?

Putting things into categories.

Examples:

• Email: Spam or Not Spam
• Fruit: Apple or Orange
• Tumor: Benign or Malignant

Popular Algorithms

Logistic Regression: Simple, fast
Decision Trees: Easy to visualize
Random Forest: More accurate
SVM: Good for complex data
Neural Networks: Most powerful

Simple Example

Classify fruits based on weight and color:

from sklearn.tree import DecisionTreeClassifier

# Data: [weight_grams, color_score]
X = [[150, 1], [170, 1], [140, 0], [130, 0]]
y = ['apple', 'apple', 'orange', 'orange']

# Train
model = DecisionTreeClassifier()
model.fit(X, y)

# Predict
new_fruit = [[160, 1]]
result = model.predict(new_fruit)
print(f"This is an: {result[0]}")

Binary vs Multi-class

Binary: 2 categories (Yes/No)
Multi-class: 3+ categories (Apple/Orange/Banana)

Evaluation Metrics

Accuracy: How many correct predictions
Precision: True positives / All positives
Recall: True positives / All actual positives

Real Applications

• Medical diagnosis
• Credit card fraud detection
• Face recognition
• Customer churn prediction

Remember

• Choose algorithm based on data
• Start with simple models
• Evaluate properly