rxFastTrees: Fast Tree


Machine Learning Fast Tree


  rxFastTrees(formula = NULL, data, type = c("binary", "regression"),
    numTrees = 100, numLeaves = 20, learningRate = 0.2, minSplit = 10,
    exampleFraction = 0.7, featureFraction = 1, splitFraction = 1,
    numBins = 255, firstUsePenalty = 0, gainConfLevel = 0,
    unbalancedSets = FALSE, trainThreads = 8, randomSeed = NULL,
    mlTransforms = NULL, mlTransformVars = NULL, rowSelection = NULL,
    transforms = NULL, transformObjects = NULL, transformFunc = NULL,
    transformVars = NULL, transformPackages = NULL, transformEnvir = NULL,
    blocksPerRead = rxGetOption("blocksPerRead"),
    reportProgress = rxGetOption("reportProgress"), verbose = 2,
    computeContext = rxGetOption("computeContext"),
    ensemble = ensembleControl(), ...)



The formula as described in rxFormula. Interaction terms and F() are not currently supported in the MicrosoftML.


A data source object or a character string specifying a .xdf file or a data frame object.


A character string that specifies the type of Fast Tree: "binary" for the default Fast Tree Binary Classification or "regression" for Fast Tree Regression.


Specifies the total number of decision trees to create in the ensemble. By creating more decision trees, you can potentially get better coverage, but the training time increases. The default value is 100.


The maximum number of leaves (terminal nodes) that can be created in any tree. Higher values potentially increase the size of the tree and get better precision, but risk overfitting and requiring longer training times. The default value is 20.


Determines the size of the step taken in the direction of the gradient in each step of the learning process. This determines how fast or slow the learner converges on the optimal solution. If the step size is too big, you might overshoot the optimal solution. If the step size is too small, training takes longer to converge to the best solution.


Minimum number of training instances required to form a leaf. That is, the minimal number of documents allowed in a leaf of a regression tree, out of the sub-sampled data. A 'split' means that features in each level of the tree (node) are randomly divided. The default value is 10. Only the number of instances is counted even if instances are weighted.


The fraction of randomly chosen instances to use for each tree. The default value is 0.7.


The fraction of randomly chosen features to use for each tree. The default value is 1.


The fraction of randomly chosen features to use on each split. The default value is 1.


Maximum number of distinct values (bins) per feature. If the feature has fewer values than the number indicated, each value is placed in its own bin. If there are more values, the algorithm creates numBins bins.


The feature first use penalty coefficient. This is a form of regularization that incurs a penalty for using a new feature when creating the tree. Increase this value to create trees that don't use many features. The default value is 0.


Tree fitting gain confidence requirement (should be in the range [0,1)). The default value is 0.


If TRUE, derivatives optimized for unbalanced sets are used. Only applicable when type equal to "binary". The default value is FALSE.


The number of threads to use in training. The default value is 8.


Specifies the random seed. The default value is NULL.


Specifies a list of MicrosoftML transforms to be performed on the data before training or NULL if no transforms are to be performed. See featurizeText, categorical, and categoricalHash, for transformations that are supported. These transformations are performed after any specified R transformations. The default value is NULL.


Specifies a character vector of variable names to be used in mlTransforms or NULL if none are to be used. The default value is NULL.


Specifies the rows (observations) from the data set that are to be used by the model with the name of a logical variable from the data set (in quotes) or with a logical expression using variables in the data set. For example, rowSelection = "old" will only use observations in which the value of the variable old is TRUE. rowSelection = (age > 20) & (age < 65) & (log(income) > 10) only uses observations in which the value of the age variable is between 20 and 65 and the value of the log of the income variable is greater than 10. The row selection is performed after processing any data transformations (see the arguments transforms or transformFunc). As with all expressions, rowSelection can be defined outside of the function call using the expression function.


An expression of the form list(name = expression, ``...) that represents the first round of variable transformations. As with all expressions, transforms (or rowSelection) can be defined outside of the function call using the expression function.


A named list that contains objects that can be referenced by transforms, transformsFunc, and rowSelection.


The variable transformation function. See rxTransform for details.


A character vector of input data set variables needed for the transformation function. See rxTransform for details.


A character vector specifying additional R packages (outside of those specified in rxGetOption("transformPackages")) to be made available and preloaded for use in variable transformation functions. For example, those explicitly defined in RevoScaleR functions via their transforms and transformFunc arguments or those defined implicitly via their formula or rowSelection arguments. The transformPackages argument may also be NULL, indicating that no packages outside rxGetOption("transformPackages") are preloaded.


A user-defined environment to serve as a parent to all environments developed internally and used for variable data transformation. If transformEnvir = NULL, a new "hash" environment with parent baseenv() is used instead.


Specifies the number of blocks to read for each chunk of data read from the data source.


An integer value that specifies the level of reporting on the row processing progress:

  • 0: no progress is reported.
  • 1: the number of processed rows is printed and updated.
  • 2: rows processed and timings are reported.
  • 3: rows processed and all timings are reported.


An integer value that specifies the amount of output wanted. If 0, no verbose output is printed during calculations. Integer values from 1 to 4 provide increasing amounts of information.


Sets the context in which computations are executed, specified with a valid RxComputeContext. Currently local and RxInSqlServer compute contexts are supported.


Control parameters for ensembling.


Additional arguments to be passed directly to the Microsoft Compute Engine.


rxFastTrees is an implementation of FastRank. FastRank is an efficient implementation of the MART gradient boosting algorithm. Gradient boosting is a machine learning technique for regression problems. It builds each regression tree in a step-wise fashion, using a predefined loss function to measure the error for each step and corrects for it in the next. So this prediction model is actually an ensemble of weaker prediction models. In regression problems, boosting builds a series of such trees in a step-wise fashion and then selects the optimal tree using an arbitrary differentiable loss function.

MART learns an ensemble of regression trees, which is a decision tree with scalar values in its leaves. A decision (or regression) tree is a binary tree-like flow chart, where at each interior node one decides which of the two child nodes to continue to based on one of the feature values from the input. At each leaf node, a value is returned. In the interior nodes, the decision is based on the test "x <= v", where x is the value of the feature in the input sample and v is one of the possible values of this feature. The functions that can be produced by a regression tree are all the piece-wise constant functions.

The ensemble of trees is produced by computing, in each step, a regression tree that approximates the gradient of the loss function, and adding it to the previous tree with coefficients that minimize the loss of the new tree. The output of the ensemble produced by MART on a given instance is the sum of the tree outputs.

* In case of a binary classification problem, the output is converted to a probability by using some form of calibration.

* In case of a regression problem, the output is the predicted value of the function.

* In case of a ranking problem, the instances are ordered by the output value of the ensemble.

If type is set to "regression", a regression version of FastTree is used. If set to "ranking", a ranking version of FastTree is used. In the ranking case, the instances should be ordered by the output of the tree ensemble. The only difference in the settings of these versions is in the calibration settings, which are needed only for classification.


  • rxFastTrees: A rxFastTrees object with the trained model.

  • FastTree: A learner specification object of class maml for the Fast Tree trainer.


This algorithm is multi-threaded and will always attempt to load the entire dataset into memory.


Microsoft Corporation Microsoft Technical Support


Wikipedia: Gradient boosting (Gradient tree boosting)

Greedy function approximation: A gradient boosting machine.

See Also

rxFastForest, rxFastLinear, rxLogisticRegression, rxNeuralNet, rxOneClassSvm, featurizeText, categorical, categoricalHash, rxPredict.mlModel.


 # Estimate a binary classification tree
 infert1 <- infert
 infert1$isCase = (infert1$case == 1)
 treeModel <- rxFastTrees(formula = isCase ~ age + parity + education + spontaneous + induced,
         data = infert1)

 # Create xdf file with per-instance results using rxPredict
 xdfOut <- tempfile(pattern = "scoreOut", fileext = ".xdf")
 scoreDS <- rxPredict(treeModel, data = infert1,
    extraVarsToWrite = c("isCase", "Score"), 
    outData = xdfOut)

 rxDataStep(scoreDS, numRows = 10)

 # Clean-up

 # Estimate a regression fast tree

 # Use the built-in data set 'airquality' to create test and train data
 DF <- airquality[!is.na(airquality$Ozone), ]  
 DF$Ozone <- as.numeric(DF$Ozone)
 randomSplit <- rnorm(nrow(DF))
 trainAir <- DF[randomSplit >= 0,]
 testAir <- DF[randomSplit < 0,]
 airFormula <- Ozone ~ Solar.R + Wind + Temp

 # Regression Fast Tree for train data
 fastTreeReg <- rxFastTrees(airFormula, type = "regression", 
     data = trainAir)  

 # Put score and model variables in data frame
 fastTreeScoreDF <- rxPredict(fastTreeReg, data = testAir, 
     writeModelVars = TRUE)

 # Plot actual versus predicted values with smoothed line
 rxLinePlot(Score ~ Ozone, type = c("p", "smooth"), data = fastTreeScoreDF)