I'm new to data mining using WEKA.
I was trying out datasets with a large dataset (2000+ attributes with 90 instances) and left the default parameters as it is.
Why is Multilayer Perceptron running long on a dataset with 2000+ attributes? K-Nearest Neighbour does a better job in terms of speed given the same dataset.
How does the hiddenLayer in MLP affect the speed and accuracy of the training set?
What is the most recommended way in running such large dataset, or is there none?