Data Science and Machine Learning (Theory and Projects) A to Z - Multiple Random Variables: Curse of Dimensionality

The video discusses the challenges of estimating probability distributions with random variables, focusing on the use of histograms for single and multivariable data. It highlights the curse of dimensionality, where more variables require exponentially more data to build reliable models. Solutions like PCA and dimensionality reduction are briefly mentioned.

How does the number of bins affect the estimation of a probability model?

What might happen if there is insufficient data for certain bins in a histogram?

What is one method mentioned for dealing with the curse of dimensionality?