Comparative Analysis of Deterministic and Nondeterministic Decision Tree Complexity Local Approach

2005 ◽  
pp. 125-143 ◽  
Author(s):  
Mikhail Ju. Moshkov
Keyword(s):  
Comparative Analysis ◽  
Decision Tree ◽  
Local Approach ◽  
Decision Tree Complexity

Gems in decision tree complexity revisited

2013 ◽  
Vol 44 (3) ◽  
pp. 42-55
Author(s):  
Raghav Kulkarni
Keyword(s):  
Decision Tree ◽  
Decision Tree Complexity

scholarly journals Performance Analysis of Predictive Models using Generic Datasets

2020 ◽  
Vol 9 (3) ◽  
pp. 3612-3617
Keyword(s):  
Neural Networks ◽  
Comparative Analysis ◽  
Performance Analysis ◽  
Decision Tree ◽  
Predictive Models ◽  
Misclassification Rate ◽  
Computational Time ◽  
Data Sets ◽  
Tree Algorithms

Today over 2.5 quintillion bytes of data is being created every single day where 753 crore people on this planet are creating 1.7mb of data each second. Most often than not, Researchers only scratch the surface when it comes to analyzing which algorithm will be best suited with their dataset and which one will give the highest efficiency. Sometimes, this analysis takes more computational time than the actual execution itself. Aim of this paper is to understand and solve this dilemma by applying different predictions models like Neural Networks, Regression and Decision Tree algorithms to different datasets where their performance was measured using ROC Index, Average Square Error and Misclassification Rate. A comparative analysis is done to show their best performance in different scopes and conditions. All data sets and results were compared and analyzed using SAS tool.

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