predict.MOA_trainedmodel | R Documentation |
Predict using a MOA classifier, MOA regressor or MOA recommender on a new dataset. \
Make sure the new dataset has the same structure
and the same levels as get_points
returns on the datastream which was used in trainMOA
## S3 method for class 'MOA_trainedmodel' predict(object, newdata, type = "response", transFUN = object$transFUN, na.action = na.fail, ...)
object |
an object of class |
newdata |
a data.frame with the same structure and the same levels as used in |
type |
a character string, either 'response' or 'votes' |
transFUN |
a function which is used on |
na.action |
passed on to model.frame when constructing the model.matrix from |
... |
other arguments, currently not used yet |
A matrix of votes or a vector with the predicted class for MOA classifier or MOA regressor. A
trainMOA
## Hoeffdingtree hdt <- HoeffdingTree(numericEstimator = "GaussianNumericAttributeClassObserver") data(iris) ## Make a training set iris <- factorise(iris) traintest <- list() traintest$trainidx <- sample(nrow(iris), size=nrow(iris)/2) traintest$trainingset <- iris[traintest$trainidx, ] traintest$testset <- iris[-traintest$trainidx, ] irisdatastream <- datastream_dataframe(data=traintest$trainingset) ## Train the model hdtreetrained <- trainMOA(model = hdt, Species ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width, data = irisdatastream) ## Score the model on the holdoutset scores <- predict(hdtreetrained, newdata=traintest$testset[, c("Sepal.Length","Sepal.Width","Petal.Length","Petal.Width")], type="response") str(scores) table(scores, traintest$testset$Species) scores <- predict(hdtreetrained, newdata=traintest$testset, type="votes") head(scores) ## Prediction based on recommendation engine require(recommenderlab) data(MovieLense) x <- getData.frame(MovieLense) x$itemid <- as.integer(as.factor(x$item)) x$userid <- as.integer(as.factor(x$user)) x$rating <- as.numeric(x$rating) x <- head(x, 2000) movielensestream <- datastream_dataframe(data=x) movielensestream$get_points(3) ctrl <- MOAoptions(model = "BRISMFPredictor", features = 10) brism <- BRISMFPredictor(control=ctrl) mymodel <- trainMOA(model = brism, rating ~ userid + itemid, data = movielensestream, chunksize = 1000, trace=TRUE) overview <- summary(mymodel$model) str(overview) predict(mymodel, head(x, 10), type = "response") x <- expand.grid(userid=overview$users[1:10], itemid=overview$items) predict(mymodel, x, type = "response")
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