Arbitrary categorisation - learning decision trees, Computer Engineering

Assignment Help:

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.


Related Discussions:- Arbitrary categorisation - learning decision trees

Describe the graphic accelerators, Q. Describe the Graphic Accelerators? ...

Q. Describe the Graphic Accelerators? A Graphic Accelerator is actually a chip as a matter of fact most significant chip in your video card. The Graphic Accelerator is essentia

Truth tables - artificial intelligence, Truth Tables - artificial intellige...

Truth Tables - artificial intelligence: In propositional logic, where we are limited to expressing sentences where propositions are true or false - we can check whether a speci

Internet data synchronization, want to know about latest work and research ...

want to know about latest work and research papers on internet data synchronization

Define cache memory, Define cache memory? A special very high speed mem...

Define cache memory? A special very high speed memory known as a cache is sometimes used to increase the speed of processing by making current programs and data available to th

Analysis of website, In this part you are required to review and critique a...

In this part you are required to review and critique a website of a café or a restaurant of your choice. Your report should be a minimum 500 words with a maximum of 1000 words. You

In what way interrupts are classified in 8085, 8085 the interrupts are clas...

8085 the interrupts are classified as Software and Hardware interrupts.

C, solution for oadovan string inc language

solution for oadovan string inc language

Set up a standard population model structure, Set up a standard population ...

Set up a standard population model structure. The population will begin with 24 people. We do not have actual data to support a birth rate, but we could calculate a reasona

Example of time division multiplexing, Two computers using TDM take up turn...

Two computers using TDM take up turns to send 100-bytes packet over a shared channel that operates at 64000 bits per second. The hardware takes 100 microseconds after one computer

Explain rudimentary file formats, Q. Explain rudimentary file formats? ...

Q. Explain rudimentary file formats? FTP only understands two rudimentary file formats. It classifies every file either as a text file or a binary file. A text file comprise a

Write Your Message!

Captcha
Free Assignment Quote

Assured A++ Grade

Get guaranteed satisfaction & time on delivery in every assignment order you paid with us! We ensure premium quality solution document along with free turntin report!

All rights reserved! Copyrights ©2019-2020 ExpertsMind IT Educational Pvt Ltd