Day 13 - Understanding Supervised Learning: Learning from Labeled Data

Context:

Today, I explored the concept of Supervised Learning, one of the foundational approaches in machine learning.

What I Learned:

• Supervised Learning is like teaching with an answer key — the model learns from labeled data.
• Formula: Training Data + Labels → Model → Predict / Classify
• Prediction (Numerical values):
  • Example: Predicting house prices
  • Algorithm: Linear Regression
• Classification (Categories/Labels):
  • Example: Spam vs. Not Spam emails
  • Algorithms: Logistic Regression, Decision Trees, Random Forests

Why It Matters for QA / AI Testing:

• Understanding supervised learning helps testers validate AI-driven predictions and classifications.
• Knowing the algorithms behind predictions ensures better test coverage for edge cases.
• Helps design test scenarios for both numeric predictions and categorical classifications.

My Takeaway:

Supervised Learning = Learn from the past to predict the future.