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Condition before learning Naive Bayes
Headtrouser
Member Posts: 2 
Learner I
Hello Community
I Want to create a Naive Bayes Model with Conditions.
The Target of the Algorithm should be to min. the misclassification and have the condition:
covariance (of a value) >= x
(To calculate the covariance its needed to have a prediction(1) value ((value - avarage of this attribute )*prediction(1)). After i got the first prediction i already can calculate it with macros)
in Naive Bayes arent any parameters to change to optimize the model.
my first sugesstion was loops in trainingprocess with branches to check condition and find model with lowest misclassification.
the problem in here is that Naive bayes wont change model becouse no parameters to change.
So i need any Ideas how to make Conditions in learning process.
Btw.
I would like to get a solution without java
I only have basic understanding of it and i am realy not good enougth to program any classificator with it.
thank you
I Want to create a Naive Bayes Model with Conditions.
The Target of the Algorithm should be to min. the misclassification and have the condition:
covariance (of a value) >= x
(To calculate the covariance its needed to have a prediction(1) value ((value - avarage of this attribute )*prediction(1)). After i got the first prediction i already can calculate it with macros)
in Naive Bayes arent any parameters to change to optimize the model.
my first sugesstion was loops in trainingprocess with branches to check condition and find model with lowest misclassification.
the problem in here is that Naive bayes wont change model becouse no parameters to change.
So i need any Ideas how to make Conditions in learning process.
Btw.
I would like to get a solution without java
I only have basic understanding of it and i am realy not good enougth to program any classificator with it.
thank you
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