If you are wondering what a Decision Tree - A Supervised Machine Learning Method is, here is a good video for a starter. Random Forest included.

One thing though, you got to use a bit of imagination when the lecture is talking about a specific example, without seeing the whiteboard!

Do let me know if you have found a better one. Thanks.



What's interesting in this video:

• build tree with attributes based on entropy
• avoid over-fitting, by 
• stop growing the tree when it's statistically insignificant
• prune
• how to deal with continuous attributes? Threshold
• beware of singleton
• pitfall of entropy - try Gain Ratio
• Pros:
• Interpretable
• Good at handling noise
• Good at handling missing data
• fast and compact
• Cons:
• can only separate data with straight lines
• greedy, leading to over-fit, but can prune or stop growing when information gain is insignificant
• Random Forest:
• a group of trees, grown like Decision Tree, with two differences:
• grow with a subset of test data
• grow with a subset of attributes
• vote - majority wins