The ore.odmSVM function builds an Oracle Data Mining Support Vector Machine (SVM) model. SVM is a powerful, state-of-the-art algorithm with strong theoretical foundations based on the Vapnik-Chervonenkis theory. SVM has strong regularization properties. Regularization refers to the generalization of the model to new data.
SVM models have similar functional form to neural networks and radial basis functions, both popular data mining techniques.
SVM can be used to solve the following problems:
Classification: SVM classification is based on decision planes that define decision boundaries. A decision plane is one that separates between a set of objects having different class memberships. SVM finds the vectors (“support vectors") that define the separators that give the widest separation of classes.
SVM classification supports both binary and multiclass targets.
Regression: SVM uses an epsilon-insensitive loss function to solve regression problems.
SVM regression tries to find a continuous function such that the maximum number of data points lie within the epsilon-wide insensitivity tube. Predictions falling within epsilon distance of the true target value are not interpreted as errors.
Anomaly Detection: Anomaly detection identifies identify cases that are unusual within data that is seemingly homogeneous. Anomaly detection is an important tool for detecting fraud, network intrusion, and other rare events that may have great significance but are hard to find.
Anomaly detection is implemented as one-class SVM classification. An anomaly detection model predicts whether a data point is typical for a given distribution or not.
The ore.odmSVM function builds each of these three different types of models. Some arguments apply to classification models only, some to regression models only, and some to anomaly detection models only.
For information on the ore.odmSVM function arguments, invoke help(ore.odmSVM).
Example 4-19 Using the ore.odmSVM Function and Generating a Confusion Matrix
This example demonstrates the use of SVM classification. The example creates mtcars in the database from the R mtcars data set, builds a classification model, makes predictions, and finally 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$ID <- 1:nrow(m) mtcars_of <- ore.push(m) svm.mod <- ore.odmSVM(gear ~ .-ID, mtcars_of, "classification") summary(svm.mod) svm.res <- predict (svm.mod, mtcars_of,"gear") with(svm.res, table(gear, PREDICTION)) # generate confusion matrixListing for Example 4-19
R> m <- mtcars
R> m$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>
R> svm.mod <- ore.odmSVM(gear ~ .-ID, mtcars_of, "classification")
R> summary(svm.mod)
Call:
ore.odmSVM(formula = gear ~ . - ID, data = mtcars_of, type = "classification")
Settings:
value
prep.auto on
active.learning al.enable
complexity.factor 0.385498
conv.tolerance 1e-04
kernel.cache.size 50000000
kernel.function gaussian
std.dev 1.072341
Coefficients:
[1] No coefficients with gaussian kernel
R> svm.res <- predict (svm.mod, mtcars_of,"gear")
R> with(svm.res, table(gear, PREDICTION)) # generate confusion matrix
PREDICTION
gear 3 4
3 12 3
4 0 12
5 2 3
Example 4-20 Using the ore.odmSVM Function and Building a Regression Model
This example demonstrates SVM regression. The example creates a data frame, pushes it to a table, and then builds a regression model; note that ore.odmSVM specifies a linear kernel.
x <- seq(0.1, 5, by = 0.02) y <- log(x) + rnorm(x, sd = 0.2) dat <-ore.push(data.frame(x=x, y=y)) # Build model with linear kernel svm.mod <- ore.odmSVM(y~x,dat,"regression", kernel.function="linear") summary(svm.mod) coef(svm.mod) svm.res <- predict(svm.mod,dat, supplemental.cols="x") head(svm.res,6)Listing for Example 4-20
R> x <- seq(0.1, 5, by = 0.02)
R> y <- log(x) + rnorm(x, sd = 0.2)
R> dat <-ore.push(data.frame(x=x, y=y))
R>
R> # Build model with linear kernel
R> svm.mod <- ore.odmSVM(y~x,dat,"regression", kernel.function="linear")
R> summary(svm.mod)
Call:
ore.odmSVM(formula = y ~ x, data = dat, type = "regression",
kernel.function = "linear")
Settings:
value
prep.auto on
active.learning al.enable
complexity.factor 0.620553
conv.tolerance 1e-04
epsilon 0.098558
kernel.function linear
Residuals:
Min. 1st Qu. Median Mean 3rd Qu. Max.
-0.79130 -0.28210 -0.05592 -0.01420 0.21460 1.58400
Coefficients:
variable value estimate
1 x 0.6637951
2 (Intercept) 0.3802170
R> coef(svm.mod)
variable value estimate
1 x 0.6637951
2 (Intercept) 0.3802170
R> svm.res <- predict(svm.mod,dat, supplemental.cols="x")
R> head(svm.res,6)
x PREDICTION
1 0.10 -0.7384312
2 0.12 -0.7271410
3 0.14 -0.7158507
4 0.16 -0.7045604
5 0.18 -0.6932702
6 0.20 -0.6819799
Example 4-21 Using the ore.odmSVM Function and Building an Anomaly Detection Model
This example demonstrates SVN anomaly detection. It uses mtcars_of created in the classification example and builds an anomaly detection model.
svm.mod <- ore.odmSVM(~ .-ID, mtcars_of, "anomaly.detection") summary(svm.mod) svm.res <- predict (svm.mod, mtcars_of, "ID") head(svm.res) table(svm.res$PREDICTION)Listing for Example 4-21
R> svm.mod <- ore.odmSVM(~ .-ID, mtcars_of, "anomaly.detection")
R> summary(svm.mod)
Call:
ore.odmSVM(formula = ~. - ID, data = mtcars_of, type = "anomaly.detection")
Settings:
value
prep.auto on
active.learning al.enable
conv.tolerance 1e-04
kernel.cache.size 50000000
kernel.function gaussian
outlier.rate .1
std.dev 0.719126
Coefficients:
[1] No coefficients with gaussian kernel
R> svm.res <- predict (svm.mod, mtcars_of, "ID")
R> head(svm.res)
'0' '1' ID PREDICTION
Mazda RX4 0.4999405 0.5000595 1 1
Mazda RX4 Wag 0.4999794 0.5000206 2 1
Datsun 710 0.4999618 0.5000382 3 1
Hornet 4 Drive 0.4999819 0.5000181 4 1
Hornet Sportabout 0.4949872 0.5050128 5 1
Valiant 0.4999415 0.5000585 6 1
R> table(svm.res$PREDICTION)
0 1
5 27