Algorithm	Accuracy	Sensitivity/Recall	Precision	F-Measure	ROC Area
Rule-based
RIPPER(JRip)	80.8581%	0.809	0.809	0.809	0.805
PART	78.5479%	0.785	0.786	0.786	0.781
Decision Table	81.8482%	0.818	0.823	0.817	0.867
Trees
Random Forest	82.1782%	0.822	0.822	0.821	0.903
J48	78.5479%	0.785	0.785	0.785	0.804
Random Tree	74.5875%	0.746	0.745	0.745	0.748
Functions
Artificial Neural Network (multilayer perception)	78.8779%	0.789	0.789	0.788	0.869
Simple Logistics	83.8284%	0.838	0.838	0.838	0.896
Bayes
Nave Bayes	83.4983%	0.835	0.836	0.834	0.894

Analyze the results in the table above. Explain in detail the performance of the above classifiers by comparing the classifier types . Select 1 classifier that is better suited for the dataset, that you wish to recommend, based on what measure(s) and why.