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
What should the size of ''t'' in btree be depending on the hard disk size

The NAND gate. The NAND gate is equivalent to an AND gate followed by a NOT gate so that the output is 0 when all of the inputs are high, otherwise the output is 1. There may

How do we synthesize Verilog into gates with Synopsys?  The answer can, of course, occupy various lifetimes to completely answer.. BUT.. a straight-forward Verilog module can b

Explain the working of a demultiplexer with the help of an example. Ans: 1:4 Demultiplexer: Fig.(a) demonstrates the logic circuit of a 1:4 demultiplexer. This has two NOT

In critical computer applications the correctness of a delivered output and the continuity of the required service beside the speed of the used CPU are the most important measures

Q. Write a menu driven program to find 9's and 10's complement of a decimal number using file. Perform necessary validation with proper message that entered numbers must be de

Q. Benefits of Device controllers? Using device controllers for attaching I/O devices to a computer system in place of connecting them directly to system bus has subsequent ben

Explain what is a transaction in SAP terminology. In SAP terminology, a transaction is series of logically linked dialog steps.

Differentiate between intranet and internet Some comparisons between intranet and internet include: -  INTERNET is INTERnational NETwork -  An INTRANET is INTernal Restri

Bernstein Conditions for Detection of Parallelism For execution of a number of instructions or a block of instructions in parallel, it must be made certain that instructions ar