Decision tree - Entropy and Information gain with Example

#decisiontree #informationgain #decisiontreeentropy
Decision tree is the most powerful and popular tool for classification and prediction. A Decision tree is a flowchart like tree structure, where each internal node denotes a test on an attribute, each branch represents an outcome of the test, and each leaf node holds a class label.
Entropy is the measure of impurity in a dataset.
Information gain is the measure of reduction in entropy. It is used to select the best splitting attribute when constructing a decision tree.

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[@ahmedpashahayathnagar5022]

Mam, What a clear and simple explanation, i have become your fan of teaching style.

[@user-qg9ue2gc5i]

You have explained very clearly . Thank you.

[@HaomaiCHEN]

讲的好

[@payalrashinkar6607]

Very neatly explained.

[@aninditadesarkar1960]

Very good explanation..top class...

[@viniciuscalixto2570]

Thank You!

[@priscaneema3308]

sooo good! thank you

[@sreebvmcreation9388]

Informative claas and thanks alot

[@abuhuraira9887]

Brilliantly Explained!. Thank You!

[@eduflairktucs7636]

Glad you liked it

[@jaigurudev4290]

Thanq Soo much mam 🙏 helped me during internals

[@b2rmathematics929]

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[@Iamsmrk_yt]

thanks mam, for sharing this informaion with us .

[@shells_o6938]

simply the best

[@surajpawar631]

cart - is used for both classification and regression problem id3-it is primarily used building classification class task

[@datascience3008]

does the split always has to be a binary split

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[@suhailjaleel7734]

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[@carearayam]

Crear explanación ❤

[@vijair6851]

Mam Kindly explain gini index also

[@str5148]

Please provide the notes u have taught.. Thank you mam🙏🙏🙏🙏

[@ravinderdavuda5785]

Does CART and ID3 both algorithms use Entropy & Information Gain to construct a decision tree

[@eduflairktucs7636]

ID3 algorithm uses information gain. CART algorithm uses gini index.

[@manaraltameemi8695]

Do you do tutoring?

[@pabbasaideepthi6454]

can u upload this notes

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Material pls

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Please upload the notes

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Gain(s,weight) = 0.37