[R] Which is the final model for a Boosted Regression Trees (GBM)?

[Bert Gunter]

You need to read the papers referenced in the Help file. Except in
trivial cases, there are NO simple models that you can fit by hand.
Like many machine learning algorithms.

-- Bert

On Sat, Jun 22, 2013 at 6:18 PM, Kristi Glover
<kristi.glover at hotmail.com> wrote:
> Hi R User,
> I was trying to find a final model in the following example by using the Boosted regression trees (GBM). The program gives the fitted values but I wanted to calculate the fitted value by hand to understand in depth. Would you give moe some hints on what is the final model for this example?
> Thanks
>
> KG
> -------
> The following script I used
> #-----------------------
> library(dismo)
> data(Anguilla_train)
> head(Anguilla_train)
> angaus.tc5.lr01 <- gbm.step(data=Anguilla_train, gbm.x = 3:13, gbm.y = 2,
> +                         family = "bernoulli", tree.complexity = 5,
> +                         learning.rate = 0.01, bag.fraction = 0.5)
>
> names(angaus.tc5.lr01)
>  [1] "initF"                "fit"
>  [3] "train.error"          "valid.error"
>  [5] "oobag.improve"        "trees"
>  [7] "c.splits"             "bag.fraction"
>  [9] "distribution"         "interaction.depth"
> [11] "n.minobsinnode"       "n.trees"
> [13] "nTrain"               "response.name"
> [15] "shrinkage"            "train.fraction"
> [17] "var.levels"           "var.monotone"
> [19] "var.names"            "var.type"
> [21] "verbose"              "data"
> [23] "Terms"                "cv.folds"
> [25] "gbm.call"             "fitted"
> [27] "fitted.vars"          "residuals"
> [29] "contributions"        "self.statistics"
> [31] "cv.statistics"        "weights"
> [33] "trees.fitted"         "training.loss.values"
> [35] "cv.values"            "cv.loss.ses"
> [37] "cv.loss.matrix"       "cv.roc.matrix"
>
>         [[alternative HTML version deleted]]
>
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> and provide commented, minimal, self-contained, reproducible code.



-- 

Bert Gunter
Genentech Nonclinical Biostatistics

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