Adding examples on xgb.importance, xgb.plot.importance and xgb.plot tree
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@ -337,6 +337,17 @@ err <- as.numeric(sum(as.integer(pred > 0.5) != label))/length(label)
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print(paste("test-error=", err))
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```
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View feature importance/influence from the learnt model
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-------------------------------------------------------
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Feature importance is similar to R gbm package's relative influence (rel.inf).
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```
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importance_matrix <- xgb.importance(model = bst)
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print(importance_matrix)
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xgb.plot.importance(importance_matrix)
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```
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View the trees from a model
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---------------------------
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@ -346,6 +357,12 @@ You can dump the tree you learned using `xgb.dump` into a text file.
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xgb.dump(bst, with.stats = T)
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```
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You can plot the trees from your model using ```xgb.plot.tree``
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```
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xgb.plot.tree(model = bst)
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```
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> if you provide a path to `fname` parameter you can save the trees to your hard drive.
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Save and load models
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