Already have an account? Get multiple benefits of using own account!
Login in your account..!
Remember me
Don't have an account? Create your account in less than a minutes,
Forgot password? how can I recover my password now!
Enter right registered email to receive password!
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:
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.
Objectives After going through this unit, you should be able to: Describe the diffrent criteria on which classification of parallel computers are based; Examine the
What is asynchronous DRAM? In asynchronous DRAM, the timing of the memory device is controlled asynchronously. A specialized memory controller circuit gives the essential contr
What is the maximum size of a database that can be opened in Microsoft Access? Ans) 1 Gigabyte
Analysis Iteration To understand any problem completely you have to repeat task which implies that analysis requires repetition. First, just get overview of problem, make a r
Data can be moved from one field to another using a 'Write:' Statement and stored in the desired format. Write: Date_1 to Date_2 format DD/MM/YY.
any ideas about senior project topic
Q. Explain Keyboard Input and Video output ? A Keystroke read from keyboard is termed as a console input and a character displayed on the video screen is known as a console out
Nonvolatile Read Write Memory, also kown as Flash memory. It is also called as shadow RAM.
Explain the term - Integrity In most cases, corporate data should remain unchanged by third parties, so the system should be capable of ensuring that only authorised personn
Q What is Cable Modem? One more way of accessing Internet currently being developed is use of cable modems. These require that you subscribe to a cable service as well as allow
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!
whatsapp: +91-977-207-8620
Phone: +91-977-207-8620
Email: [email protected]
All rights reserved! Copyrights ©2019-2020 ExpertsMind IT Educational Pvt Ltd