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How can I implement weighted Bayes Classification in RapidMiner
Contributor II
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
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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