Description
The XGBoostPredict td_xgboost_predict_mle
function applies the model output
by the XGBoost td_xgboost_mle
function to a new data set, outputting predicted
labels for each data point.
Usage
td_xgboost_predict_mle ( object = NULL, object.order.column = NULL, newdata = NULL, newdata.partition.column = "ANY", newdata.order.column = NULL, id.column = NULL, terms = NULL, iter.num = NULL, num.boosted.trees = NULL, attribute.name.column = NULL, attribute.value.column = NULL, output.response.probdist = FALSE, output.responses = NULL, newdata.sequence.column = NULL, object.sequence.column = NULL ) ## S3 method for class 'td_xgboost_mle' predict( object = NULL, object.order.column = NULL, newdata = NULL, newdata.partition.column = "ANY", newdata.order.column = NULL, id.column = NULL, terms = NULL, iter.num = NULL, num.boosted.trees = NULL, attribute.name.column = NULL, attribute.value.column = NULL, output.response.probdist = FALSE, output.responses = NULL, newdata.sequence.column = NULL, object.sequence.column = NULL )
Arguments
object |
Required Argument. |
object.order.column |
Required Argument. |
newdata |
Required Argument. |
newdata.partition.column |
Optional Argument. |
newdata.order.column |
Optional Argument. |
id.column |
Optional Argument. |
terms |
Optional Argument. |
iter.num |
Optional Argument. |
num.boosted.trees |
Optional Argument. |
attribute.name.column |
Optional Argument. |
attribute.value.column |
Optional Argument. |
output.response.probdist |
Optional Argument. |
output.responses |
Optional Argument.
Default Value: NULL |
newdata.sequence.column |
Optional Argument. |
object.sequence.column |
Optional Argument. |
Value
Function returns an object of class "td_xgboost_predict_mle" which is a
named list containing Teradata tbl object.
Named list member can be referenced directly with the "$" operator
using name: result.
Examples
# Get the current context/connection con <- td_get_context()$connection # Load example data. loadExampleData("xgboost_example", "housing_train_binary","iris_train","sparse_iris_train","sparse_iris_attribute") loadExampleData("xgboostpredict_example", "housing_test_binary","iris_test","sparse_iris_test") # Example 1: Binary Classification # Create remote tibble objects. housing_train_binary <- tbl(con, "housing_train_binary") housing_test_binary <- tbl(con,"housing_test_binary") # create model td_xgboost_out1 <- td_xgboost_mle(data=housing_train_binary, id.column='sn', formula = ( homestyle ~ driveway + recroom + fullbase + gashw + airco + prefarea + price + lotsize + bedrooms + bathrms + stories + garagepl ), num.boosted.trees=2, loss.function='binomial', prediction.type='classification', reg.lambda=1, shrinkage.factor=0.1, iter.num=10, min.node.size=1, max.depth=10 ) # Use the generated model to find prediction. td_xgboost_predict_out1 <- td_xgboost_predict_mle(newdata=housing_test_binary, object=td_xgboost_out1, object.order.column= c('tree_id','iter','class_num'), id.column='sn', terms='homestyle', num.boosted.trees=1) # Alternatively use S3 predict method to find the prediction. predict_out <- predict(td_xgboost_out1, newdata=housing_test_binary, object.order.column= c('tree_id','iter','class_num'), id.column='sn', terms='homestyle', num.boosted.trees=1) # Example 2: Multiple-Class Classification iris_train <- tbl(con,"iris_train") iris_test <- tbl(con,"iris_test") td_xgboost_out2 <- td_xgboost_mle(data=iris_train, id.column='id', formula = ( species ~ sepal_length + sepal_length + petal_length + petal_width + species), num.boosted.trees=2, loss.function='softmax', reg.lambda=1, shrinkage.factor=0.1, iter.num=10, min.node.size=1, max.depth=10) # Use the generated model to find prediction. td_xgboost_predict_out2 <- td_xgboost_predict_mle(newdata=iris_test, object=td_xgboost_out2, object.order.column=c('tree_id', 'iter', 'class_num'), id.column='id', terms='species', num.boosted.trees=2) # Example 3: Sparse Input Format. response.column argument is specified instead of formula. # Create remote tbl objects. sparse_iris_train <- tbl(con,"sparse_iris_train") sparse_iris_attribute <- tbl(con,"sparse_iris_attribute") sparse_iris_test <- tbl(con,"sparse_iris_test") td_xgboost_out3 <- td_xgboost_mle(data=sparse_iris_train, attribute.table=sparse_iris_attribute, id.column='id', attribute.name.column='attribute', attribute.value.column='value_col', response.column="species", loss.function='SOFTMAX', reg.lambda=1, num.boosted.trees=2, shrinkage.factor=0.1, column.subsampling=1.0, iter.num=10, min.node.size=1, max.depth=10, variance=0, seed=1 ) # Use the generated model to find prediction. td_xgboost_predict_out3 <- td_xgboost_predict_mle(newdata=sparse_iris_test, object=td_xgboost_out3, object.order.column=c('tree_id', 'iter', 'class_num'), id.column='id', attribute.name.column='attribute', attribute.value.column='value_col', terms='species', num.boosted.trees=2)