Reply With QuoteDear i m still working on it. but if you see that i am putting wrong answer then plz let me know too.
Mr. Xpert do something plz.
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acha aur first walay ka kia hai?
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Dear i m still working on it. but if you see that i am putting wrong answer then plz let me know too.
Mr. Xpert do something plz.
who tu jab ayen gay tu bata den gay you help here. itna short answer dia hai. is par tu 0.1 marks bhi nahen ayen gay.
but iss ka aur koi answer abi tak mila nahin my friend....really i m also thinking of it.thats y i didnt post it all.
chalen kuch tu likha app nay. bakiyon ka bhi kuch kar den. warna tu zero confirm hai.
Q2)
Decision tree learning is used in data mining and machine learning. They uses a decision tree as a predictive model which maps observations about an item to conclusions about the item's target value
Kindly reply 2 Q3
Bhai idher bounus day bi guzer gia or expert sab ke solutions mile hi nahien........ acha expert ha.
no need to say any thing to xpert bahi aur yeh aun ki maharbani hoti hia jo subject aun ka nahi hota whoe bhi try karty rehty hainOriginally Posted by shahidmus
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janab here comes the expert app naraz tu na hon na
yah len reference
second question ka reference haiCode:http://www.cis.hut.fi/harri/thesis/valpola_thesis/node34.html
yah first question hai baki main kar raha honCode:http://en.wikipedia.org/wiki/Decision_tree_learning
Q3)
Avoiding over-fitting the data
In principle decision tree algorithm described in Figure 2 can grow each branch of the tree just deeply enough to perfectly classify the training examples. While this is sometimes a reasonable strategy, in fact it can lead to difficulties when there is noise in the data, or when the number of training examples is too small or too large to produce a representative sample of the true target function. In either of these cases, this simple algorithm can produce trees that over-fit the training examples.
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