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Decision tree can be seen as a classification problem
Example 1 Dating : what attribute to split one.
Intuition 20 questions, ask broader questions at first to narrow down the domain space for example animal vs personal
Decision tree learning
1.First find the best attribute to split
For example splitting in half (binary search)
2. Ask questions depending on the answer
3. Follow the answer path
4. Go back to no.1 until an answer is found
The above describes an ALGORITHM
Best attribute to split
Depends on entropy
Does the split improve information gain
Decision nodes can be used to write boolean operation or gates logic.
AND(A,B)
A
/f \t
- B
/f\t
- +
IF an operation is communicative, that means if we switch the operands will still get the same result. AND(A,B) is the same as AND(B,A)
Extra: random forest is a collection of randomized decision trees that was selected if it generated good result. Machine continuous decision tree branch can be a fraction of 1. Machine discrete decision branch true false. Information gain formula will automatically rule out some redundancy. Pruning the tree collapse the tree and see the error get a smaller tree avoid over fitting
AND(A,B)
A
/f \t
- B
/f\t
- +
IF an operation is communicative, that means if we switch the operands will still get the same result. AND(A,B) is the same as AND(B,A)
Extra: random forest is a collection of randomized decision trees that was selected if it generated good result. Machine continuous decision tree branch can be a fraction of 1. Machine discrete decision branch true false. Information gain formula will automatically rule out some redundancy. Pruning the tree collapse the tree and see the error get a smaller tree avoid over fitting
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