Arbitrary categorisation - learning decision trees, Computer Engineering

Arbitrary categorisation - learning decision trees:

Through visualising  a set of boxes with some balls in. There if all the balls were in a single box so this would be nicely ordered but it would be extremely easy to find a particular ball. Moreover If the balls were distributed amongst the boxes then this would not be so nicely ordered but it might take rather a whereas to find a particular ball. It means if we were going to define a measure based at this notion of purity then we would want to be able to calculate a value for each box based on the number of balls in it so then take the sum of these as the overall measure. Thus we would want to reward two situations: nearly empty boxes as very neat and boxes just with nearly all the balls in as also very neat. However this is the basis for the general entropy measure that is defined follows like: 

Now next here instantly an arbitrary categorisation like C into categories c1, ..., cn and a set of examples, S, for that the proportion of examples in ci is pi, then the entropy of S is as: 

198_Arbitrary categorisation - learning decision trees.png

Here measure satisfies our criteria that is of the -p*log2(p) construction: where p gets close to zero that is the category has only a few examples in it so then the  log(p) becomes a big negative number and the  p  part dominates the calculation then the entropy works out to be nearly zero. However make it sure that entropy calculates the disorder in the data in this low score is good and as it reflects our desire to reward categories with few examples in. Such of similarly if p gets close to 1 then that's the category has most of the examples in so then the  log(p) part gets very close to zero but it  is this that dominates the calculation thus the overall value gets close to zero. Thus we see that both where the category is nearly  -  or completely  -  empty and when the category nearly contains as - or completely contains as  - all the examples and the score for the category gets close to zero that models what we wanted it to. But note that 0*ln(0) is taken to be zero by convention them.

Posted Date: 1/11/2013 6:40:03 AM | Location : United States







Related Discussions:- Arbitrary categorisation - learning decision trees, Assignment Help, Ask Question on Arbitrary categorisation - learning decision trees, Get Answer, Expert's Help, Arbitrary categorisation - learning decision trees Discussions

Write discussion on Arbitrary categorisation - learning decision trees
Your posts are moderated
Related Questions
VBA is licensed to Microsoft and this compatible with and only Microsoft products. Code written is compiled by an intermediate language known as P-code and this is stored in hostin

What are modes of operation of centralized SPC? In about all the present day electronic switching systems utilizing centralized control, only a two-processor configuration is

can i get a prepared ppt for this topic to present it in a seminar??

Design a BCD to excess 3 code converter using minimum number of NAND gates. Hint: use k map techniques. Ans. Firstly we make the truth table: BCD no

Magnetic storage - computer architecture: Magnetic storage uses different type of patterns of magnetization on a magnetically coated surface to store information. Magnetic sto

What is link destruction? Link destruction is inverse of link creation. When a link is destroyed makes sure the associated objects accessible by other handles or intentionally

LoadRunner works by making virtual users who take the place of real users operating client software, such as sending requests using the HTTP protocol to IIS or Apache web servers.

Instruction Pipelines As discussed previous, the stream of instructions in the instruction implementation cycle, can be realized through a pipeline where overlapped implementat

Don't scan at more resolution than needed. This saves both Disk and time Space. Typically itisn't useful to scan at more than optical resolution because it adds no new informa

Voice Identifier - Biometric Computer Security Systems Besides fingerprint, another popular security technology is voice identifier. According to VSS organization (2007), voic