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.
What is a thread? A thread otherwise called a lightweight process (LWP) is a basic unit of CPU utilization, it comprises of a thread id, a program counter, a register set and a
How are switching system classified
Categorized Optimization transformations The structure of program and the way in that data is defined and used in this provide vital clues for optimization. Optimization t
in building a suspension bridge a cable is to be stretched from the top of a pier to a point 852.6 ft. from it''s foot. if from this point the angle of elevation of the top of the
Subtraction of 01000-01001 using 2's complement method. Ans. Firstly 1's complement of 01001 is 10110 and 2's complement is 10110+ 1 =10111. Thus 01000 = 01000 - 01001
clasification of bus
Question: 1. In Flash, what is the purpose of a motion guide layer? 2. Differentiate between Motion and Shape tween. 3. Give the steps to create a layer mask in flash.
What is the Vertical Organizational Structure? This is a traditional approach that is typified by a functional approach to work in that departments work onto tasks relevant to
Define DMA controller. The I/O device interface control circuit that is used for direct memory access is called as DMA controller.
Describe functions of data flow diagram After you have roughly designed data flow diagram, you could write a description of each function and you could describe the function i
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