Improvement and development of C5 0 decision Tree

Abstract

newline Machine learning a phenomena in which machines are capable to make decisions and newlineable to perform intelligence tasks, to make intelligence and machine may be trained newlineusing the examples and machine use these examples to make smart decisions. The newlineimportant component of machines is search trees, Thus in this proposed work we newlinerequired to investigate about different machine learning decision trees. newlineThe decision tree is a transparent model of machine learning and for evaluation of newlineresults user can simply follow with different parameters and can find their required newlineresults, thus here we can say that decision trees are a data structure where all the data newlineare mounted over the tree. In the survey of literature it was found that the performance newlineof decision trees is degraded due to some challenges. In this work the investigation newlinehas been done about the different challenges and factors that affect any decision tree newlineperformances. Additionally some more investigation has been done about the newlineperformance improvement strategies. newlineAfter the collection and investigation, the solution steps have been taken to propose newlineand design an effective and efficient data mining tree algorithm by which the decision newlinetrees accuracy can be effectively improved. After implementation of the proposed newlinealgorithm the justification of the model is provided with the comparative study of newlineperformances with respect to C5.0 decision tree model performance under different newlineperformance parameters, i.e. accuracy, memory uses, built time, search time, and error newlinerate.