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How can I implement weighted Bayes Classification in RapidMiner

nachiketnachiket Member Posts: 6 Contributor I
edited November 2018 in Help
I am new Rapid Miner
I want to assign more weight to slope than other attributes in predicting the zone
I implemented Naive Bayes after referring to sample given for golf dataset on my data set now I want to assign weights
Sample Data(1100 rows)
(be Assign weight I mean more importance should be given to that attribute while predicting )
FID   Geology                                           Geomorphology                                               Land use_land cover    Rainfall          SLOPE    Soil                     zone
0   Fissile hornblende biotite gneiss   HIGHLY DISSECTED DIFLECTION SLOPE        FOREST                    1200-1400   >60%      BROWN CLAY          High
1   Fissile hornblende biotite gneiss   HIGHLY DISSECTED DIFLECTION SLOPE        FOREST                    1200-1400   30-60%   BROWN CLAY      Moderate
2 Charnockite                           HIGHLY DISSECTED DIFLECTION SLOPE     SEMI EVERGREEN     1200-1400  30-60%  BROWN CLAY High
3 Charnockite                           HIGHLY DISSECTED DIFLECTION SLOPE BUILDUP,RURAL     1200-1400  15-30%  BROWN CLAY High
4 Charnockite                           HIGHLY DISSECTED DIFLECTION SLOPE BUILDUP,RURAL     1200-1400  30-60%  BROWN CLAY     Very High
5 Charnockite                           LESS DESSECTED UNDULATING PLATEAU AGRICULTURE     1600-2000  5-8% ROCK OUT CROP low


When Bayes classification is done I want to predict the zone(of testing data) , I want more priority to be given to zone while predicting the zone
as there are many attributes accuracy is getting lowered is there any thing else that can be done to solve the same
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