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.
Documentation is done to give others with information and ease maintenance. The best documentation is done in the headers (function and scripts) and directly in the code. Any usefu
(a) Why did SAP introduce the extended star schema? Explain why it is reported to be better than the traditional schema model? (b) What is the difference between a dimension use
#questi on.. How it works Explain explain
Declarative programming languages: We notice that declarative programming languages can have some better compensation over procedural ones. Actually, it is often said that a J
Determine the approaches to organizing stored program control There are 2 approaches to organizing stored program control: 1. Centralized: In this control, all control equi
Lists out some applications of Shift Register. Ans: Applications of Shift Registers: a. Serial to Parallel Converter b. Parallel to Serial Converter c. Delay li
The number of control lines for 16 to 1 multiplexer is ? Ans. We have 16 = 2 4 , 4 Select lines are needed.
Q. Explain about Interlacing? Interlacing is a procedure in which in place of scanning the image one-line-at-a-time it's scanned alternatelyit implies thatalternate lines are s
minimum self program
Write a GUI/MP3 program called MP3Random that reads all MP3 les in a directory and plays them in random order. The GUI should have a little window with: 1. A button Next that s
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