Learning and testing an algorithm for generating a decision tree from mysql input.
Decision Trees are used in data mining, machine learning and artificial intelligence to look at data and perform operation such as :
- classifying data
- discovering hidden patterns in your data
- predicting future behaviors
This is a great video which covers most of the concepts I use in this repository :
"Machine learning - Decision trees" from UBC
[x] complete branching logic
[ ] create and print a real tree
[ ] flush out unit tests
[ ] run more test data through tests to validate that I am calculating IG correctly
[ ] add trace, error, info logging
[ ] contrive queries to discover relationships between data
[ ] create trees based on those queries
[ ] pretty print conclusions based on those queries
[ ] research use of using random forest
[ ] figure out if i should be branching on the same attribute twice. Would that make sense ever?
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