parameter optimization for classifier algorithm

It is said that different algorithms have different parameters. I don't really see this as true, say if it is a tree decision algorithm and naive bayesian algorithm, what开发者_运维知识库 is the parameter for each? Can someone give me an example..

If this is the case then doing a 5-fold cross validation for a data that is going to be run using a decision tree algorithm is different with bayesian?

Also for the parameter optimization I will do a 5-fold cross validation. Is there a way to automatically do this to determine the set values key of parameters using weka?

Since you are using Weka, you can see the parameters for each algorithm by opening dataset in Explorer, going to Classify, choosing algorithm and then Clicking on algorithm box. So for instance Naive Bayes classifier has parameters that affect how it deals with continuous data (discretization or using kernel estimator)

The parameters to the decision algorithm may change even as time goes on in the algorithm, and certainly between algorithms.

Let's say you have an AI decision tree for determining moving soldiers around a battle field. You may have a defensive algorithm, which will seek a decision that maximizes its own life where it can. You may have an aggressive algorithm, which will seek maximum damage against other soldiers. You may have demolition algorithms that seek structural damage to walls. Each of these will have different parameters for determining which decision to make.

And the decision parameters may change as the simulation goes on. For example, the aggressive algorithm may weigh damage done to damage taken in a 2:1 manner. Let's say the AI is willing to look 100 simulation cycles into the future to make a decision. It may find that even though it was weighing 2:1, the simulations it ran to make the decision didn't match what actually happened. If it calculated it would take 100 damage, but do 200 damage, but it actually took 150 damage, which killed it before it could barely do 70 damage, (assuming it's designed to) it could take this into consideration. Simularly, it may find that when it chose to reposition under certain conditions, it was able to avoid damage during ticks T+10, gain a vantage point, and do more damage during ticks T+40 to T+80 than it would have normally. This will cause it to consider the safer situations more than it would have before.