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

Determine about the term- voice synthesis, Voice synthesis Loud speakers ...

Voice synthesis Loud speakers and special software are used to output information in the form of sound to help blind and partially-sighted people; it also helps people who have d

the monte carlo simulation technique, The demand placed on a system is exp...

The demand placed on a system is explained by a lognormally distributed random variable with mean 50 and standard deviation of 10. The capacity of the system is modeled by a Weibul

Why a computer expect to receive responses, Why a computer expect to receiv...

Why a computer expect to receive responses when it broadcast an ARP request? Response will be acquired only from the machine for that request is being sent not for the other ma

Ellipse follows the perimeter of the window, A) Execute a program where an ...

A) Execute a program where an ellipse follows the perimeter of the window. B)  Execute a program that can draw graphs, possibly following your plan from last week. Have it graph

Nix commands, reate a directory "Unix" under your home directory. Command(...

reate a directory "Unix" under your home directory. Command(s): ………………………………………….

Define colour depth in graphic display system, Q. Define Colour Depth in gr...

Q. Define Colour Depth in graphic display system? It is clear that an image contains an array of pixels.  If we tell which pixels are 'off' and which are 'on' to the monitor, i

State about the firewalls - intranet, Firewalls While getting one fire...

Firewalls While getting one firewall for the company's Intranet it should be well known that firewalls come in both hardware and software forms, and that even though all firew

Explain briefly the generic framework for e-commerce, Explain briefly the g...

Explain briefly the generic framework for e-commerce.  Generic framework of e-commerce contains the Applications of EC   (like as banking, shopping in online stores and malls,

Calculate quantities from information in bayesian network, 1. A Bayesian ne...

1. A Bayesian network is shown for the variables paper Thickness, paper Alignment and Print Quality. The conditional probabilities are provided in the tables beside the nodes. Here

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