The ore.odmNB function builds an Oracle Data Mining Naive Bayes model. The Naive Bayes algorithm is based on conditional probabilities. Naive Bayes looks at the historical data and calculates conditional probabilities for the target values by observing the frequency of attribute values and of combinations of attribute values.
Naive Bayes assumes that each predictor is conditionally independent of the others. (Bayes' Theorem requires that the predictors be independent.)
For information on the ore.odmNB function arguments, invoke help(ore.odmNB).
Example 4-16 Using the ore.odmNB Function
This example creates an input ore.frame, builds a Naive Bayes model, makes predictions, and generates a confusion matrix.
m <- mtcars m$gear <- as.factor(m$gear) m$cyl <- as.factor(m$cyl) m$vs <- as.factor(m$vs) m$ID <- 1:nrow(m) mtcars_of <- ore.push(m) row.names(mtcars_of) <- mtcars_of # Build the model. nb.mod <- ore.odmNB(gear ~ ., mtcars_of) summary(nb.mod) # Make predictions and generate a confusion matrix. nb.res <- predict (nb.mod, mtcars_of, "gear") with(nb.res, table(gear, PREDICTION))Listing for Example 4-10
R> m <- mtcars
R> m$gear <- as.factor(m$gear)
R> m$cyl <- as.factor(m$cyl)
R> m$vs <- as.factor(m$vs)
R> m$ID <- 1:nrow(m)
R> mtcars_of <- ore.push(m)
R> row.names(mtcars_of) <- mtcars_of
R> # Build the model.
R> nb.mod <- ore.odmNB(gear ~ ., mtcars_of)
R> summary(nb.mod)
Call:
ore.odmNB(formula = gear ~ ., data = mtcars_of)
Settings:
value
prep.auto on
Apriori:
3 4 5
0.46875 0.37500 0.15625
Tables:
$ID
( ; 26.5), [26.5; 26.5] (26.5; )
3 1.00000000
4 0.91666667 0.08333333
5 1.00000000
$am
0 1
3 1.0000000
4 0.3333333 0.6666667
5 1.0000000
$cyl
'4', '6' '8'
3 0.2 0.8
4 1.0
5 0.6 0.4
$disp
( ; 196.299999999999995), [196.299999999999995; 196.299999999999995]
3 0.06666667
4 1.00000000
5 0.60000000
(196.299999999999995; )
3 0.93333333
4
5 0.40000000
$drat
( ; 3.385), [3.385; 3.385] (3.385; )
3 0.8666667 0.1333333
4 1.0000000
5 1.0000000
$hp
( ; 136.5), [136.5; 136.5] (136.5; )
3 0.2 0.8
4 1.0
5 0.4 0.6
$vs
0 1
3 0.8000000 0.2000000
4 0.1666667 0.8333333
5 0.8000000 0.2000000
$wt
( ; 3.2024999999999999), [3.2024999999999999; 3.2024999999999999]
3 0.06666667
4 0.83333333
5 0.80000000
(3.2024999999999999; )
3 0.93333333
4 0.16666667
5 0.20000000
Levels:
[1] "3" "4" "5"
R> # Make predictions and generate a confusion matrix.
R> nb.res <- predict (nb.mod, mtcars_of, "gear")
R> with(nb.res, table(gear, PREDICTION))
PREDICTION
gear 3 4 5
3 14 1 0
4 0 12 0
5 0 1 4