random forest algorithm(05!05!14)
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Presentation about Random Forest Algorithm which is well know algorithm for classificationTRANSCRIPT
PowerPoint Presentation
Dynamic Pruning of Random Forest
Under the esteemed supervision of Mrs. Nagarjuna Devi
Y. Poorna DurgaP. Saimadhu Team members:Dynamic Pruning of Random Forest Classifier
IntroductionExisted MethodsProposed MethodRequirements analysisImplementation Results
Introduction
Random forest overviewRandom forest AlgorithmApplications of Random Forest Classifier
Existed Methods
Static Pruning Methods Methods based on diversity measures. Methods based on search algorithms.Methods based on clustering of classifiers.Methods based on heuristic rules.
Proposed Method
Dynamic pruning
Requirements analysisProgramming Languages : PythonPackages Required : NumPyTools : Weka IDE : Stani's Python Editor(SPE)Data sets : Titanic, Qualitative Bankruptcy , Cancer detection , USA voting ImplementationInformation Gain module Decision Tree moduleRandom Forest module( Including proposed method)ResultsCancer Detection with 10 attributes including target attribute Weka accuracy average of 10 results : 70.04 %Proposed method average of 10 results : 74.20 %ResultsQualitative Bankruptcy with 7 attributes including target attribute Weka accuracy average of 10 results : 92.04 %Proposed method average of 10 results : 95.32 %ResultsTime Complexity : For building a tree is O(mnlogn)Time Complexity : For building M trees O(M(mnlogn)Where :M : Number of trees.m : Number of attributes.n : The number of instances in the training data. Thank you