How to interpret the naive bayes result in weka? [closed]

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Anybody please help me to interpret the following result generated in weka for classification using naive bayes.

Please explain clearly what is

• Normal Distribution
• Mean
• StandardDev
• WeightSum
• Precision.

Please help me. I am new in weka.

** Naive Bayes Classifier

Class Normal: Prior probability = 0.5 

1374195_at:  Normal Distribution. Mean = 218.06 StandardDev = 6.0572 WeightSum = 3 Precision = 36.34333334
1373315_at:  Normal Distribution. Mean = 1142.58 StandardDev = 21.1589 WeightSum = 3 Precision = 126.95333339999999

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Normal distribution is the classic gaussian distribution. Mean and Standard deviation are properties of a normal/gaussian distribution. Look to basic statistics texts about this.

Weight Sum. This value is calculated for numerical values. Its value is equal to class distribution. For iris dataset there are 3 classes (50,50,50) and this value is 50 for all of them. For weather dataset it is 9 5. Same as class instance number. Your attribute value affects your result according to class distribution.

Precision : TP / (TP + FP) The percentage of positive predictions that are correct.

More resources : Classifier Evaluation