DMCT-NOTES/unit 4/01_Classification_Basics.md

Classification Basics

What is Classification?

Classification is the process of predicting the class label of a data item.

• Goal: To assign a category to a new item based on past data.
• Example:
  • Input: A bank loan application.
  • Output Class: "Safe" or "Risky".

Classification vs Prediction

• Classification: Predicts a category (Discrete value).
  • Example: Yes/No, Red/Blue/Green.
• Prediction (Regression): Predicts a number (Continuous value).
  • Example: Predicting the price of a house ($500k, $505k...).

The Process

1. Training Phase (Learning):
   • The algorithm learns from a "Training Set" where the correct answers (labels) are known.
   • It builds a Model (e.g., a Decision Tree).
2. Testing Phase (Classification):
   • The model is tested on new, unseen data ("Test Set").
   • We check the Accuracy: Percentage of correct predictions.