select_best: Selecting best model according to weights
In tsensembler: Dynamic Ensembles for Time Series Forecasting
Description
Usage
Arguments
See Also
View source: R/ensembling-pipes.R
Description
This function select the best model from a matrix of data x models.
For each row (data point), the model with maximum weight is assigned
a weight of 1, while the remaining models are assigned a weight of 0.
Usage
1
select_best(model_scores)
Arguments
model_scores
matrix containing the model weights
across the observations
See Also
Other weighting base models:
EMASE(),
build_committee(),
get_top_models(),
model_recent_performance(),
model_weighting()
tsensembler documentation built on Oct. 27, 2020, 5:07 p.m.
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Description Usage Arguments See Also
View source: R/ensembling-pipes.R
Description
This function select the best model from a matrix of data x models. For each row (data point), the model with maximum weight is assigned a weight of 1, while the remaining models are assigned a weight of 0.
Usage
1 | select_best(model_scores)
|
Arguments
model_scores |
matrix containing the model weights across the observations |
See Also
Other weighting base models:
EMASE(),
build_committee(),
get_top_models(),
model_recent_performance(),
model_weighting()
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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