getTerminalNodeIDs
(Package: ranger) :
Get terminal node IDs of observations.
Get terminal node IDs of observations.
● Data Source:
CranContrib
● Keywords:
● Alias: getTerminalNodeIDs
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Prediction with new data and a saved forest from Ranger.
● Data Source:
CranContrib
● Keywords:
● Alias: predict.ranger
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Print contents of Ranger prediction object.
● Data Source:
CranContrib
● Keywords:
● Alias: print.ranger.prediction
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holdoutRF
(Package: ranger) :
Hold-out random forests
Grow two random forests on two cross-validation folds. Instead of out-of-bag data, the other fold is used to compute permutation importance. Related to the novel permutation variable importance by Janitza et al. (2015).
● Data Source:
CranContrib
● Keywords:
● Alias: holdoutRF
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Extract unique death times of Ranger Survival forest
● Data Source:
CranContrib
● Keywords:
● Alias: timepoints, timepoints.ranger
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importance_pvalues
(Package: ranger) :
ranger variable importance confidence intervals and p-values
Compute variable importance with confidence intervals and p-values.
● Data Source:
CranContrib
● Keywords:
● Alias: importance_pvalues
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Ranger is a fast implementation of Random Forest (Breiman 2001) or recursive partitioning, particularly suited for high dimensional data. Classification, regression, and survival forests are supported. Classification and regression forests are implemented as in the original Random Forest (Breiman 2001), survival forests as in Random Survival Forests (Ishwaran et al. 2008).
● Data Source:
CranContrib
● Keywords:
● Alias: ranger
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Prediction with new data and a saved forest from Ranger.
● Data Source:
CranContrib
● Keywords:
● Alias: predict.ranger.forest
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Extract predictions of Ranger prediction object.
● Data Source:
CranContrib
● Keywords:
● Alias: predictions, predictions.ranger.prediction
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Extract unique death times of Ranger Survival prediction object.
● Data Source:
CranContrib
● Keywords:
● Alias: timepoints.ranger.prediction
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