Foundations for Machine Learning | Bayes Theorem – Intuition and basics [Video]

Bayes’ Theorem: A Powerful Tool for Decision-Making

Bayes’ Theorem is a cornerstone of probability theory, helping us update beliefs when new evidence is introduced. It is not just about the math—it is about intuition. Let me illustrate this with a real-world example.

Imagine a mammogram test for breast cancer:

-1% of women in a population have breast cancer.

-The test detects cancer correctly 80% of the time.

-The test shows false positives 9.6% of the time.

If a woman gets a positive result, what is the probability she actually has cancer? While the test is 80% accurate, the probability of having cancer given a positive test is only 7.76%! Why? Because false positives (from the 99% who do not have cancer) heavily skew the results.

This is where Bayes’ Theorem shines. It accounts for prior probabilities (how common cancer is) and adjusts for the test’s imperfections. The equation transforms complex situations into actionable insights.

Beyond healthcare, Bayes’ Theorem powers spam filters, recommendation systems, and machine learning models, shaping smarter decisions in uncertain scenarios.

Learn more from my lecture on Vizuara’s YouTube channel: https://youtu.be/mhX9ktDPZrw