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
Q. Evaluate Physical address of top of stack? Value of stack segment register (SS) = 6000h Value of stack pointer (SP) which is Offset = 0010h  So Physical address of top

"Super ASCII", if it contains the character frequency equal to their ascii values. String will contain only lower case alphabets (''a''-''z'') and the ascii values will starts from


Design a counter modulo 4 (sequential circuit with two flip-flops and one input U) which work like that: 1. When U=0, the state of the flip-flop does not change. 2. Whe

What are the two primary models of Supply Chain Management? The Two Primary models of Supply Chain Management are:- 1.  Porter's Value Chain Model 2. Supply Chain Model

What is branch folding? The instruction fetch unit has implemented the branch instruction concurrently with the implementation of other instructions. This technique is referred

Q. Example to show directory in doc? Like a file name, the directory name may also have up to eight alpha-numeric characters. The directory name can also have an extension

What is the significance of Technical settings (specified while creating a table in the data dictionary)?  By specifying technical settings we can handle how database tables ar

Q. Example to show a single digit ? Displaying a single digit (0 to 9) Presume that a value 5 is stored in BL register then to output BL as ASCII value add character ‘0' to

Q. Illustrate when are intermolecular forces the strongest? Answer:- Intermolecular forces dispersion and dipole-dipole and hydrogen bonds. These forces are feeble than ch