| predict.RandomForestRegressor {hana.ml.r} | R Documentation |
Predict based on model for Random Forest Regressor
Description
Predict based on model for Random Forest Regressor
Usage
## S3 method for class 'RandomForestRegressor'
predict(model, data, key,
features = NULL, verbose = NULL, block.size = NULL,
missing.replacement = NULL)
Arguments
model |
model for predict
|
data |
DataFrame Independent variable values used for prediction.
|
key |
character Name of the ID column.
|
features |
list of character, optional
Names of the feature columns.
If not provided, it defaults to all non-ID columns.
|
verbose |
logical , optional
Defaults to FALSE. Valid only for Classification.
|
block.size |
integer, optional
The number of rows loaded per time during prediction.
0 indicates load all data at once. Defaults to 0.
|
missing.replacement |
character, optional
The missing replacement strategy:
- 'feature_marginalized': marginalise each missing feature out
independently.
- 'instance_marginalized': marginalise all missing features
in an instance as a whole corresponding to each category.
Defaults to feature_marginalized.
|
Value
DataFrame of predicted values, structured as follows.
ID column, with same name and type as ‘data'’s ID column.
SCORE, type NVARCHAR, predicted values.
CONFIDENCE, type DOUBLE. Representing the confidence of a class. All 0s for regression.
[Package
hana.ml.r version 1.0.8
Index]