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Stack weights from a fitted ensemble

Source: R/learners.R
stack_weights.Rd

Reports how much each base learner contributed to a "stack" model, which is usually more informative than the ensemble's accuracy alone: a stack that puts all its weight on one learner is telling you the others added nothing.

Usage

stack_weights(model)

Arguments

model

A model fitted by train_model() with algorithm = "stack".

Value

A named numeric vector of weights summing to one.

See also

train_model(), list_learners()

Examples

p <- agri_project(demo_agri_data(n_units = 14, n_seasons = 3))
p <- build_features(phenology_windows(add_climate(p)), stats = "sum")
set.seed(1)
m <- train_model(p, "yield", algorithm = "stack", k = 3,
                 compare_random = FALSE)
#> Warning: 24 features for 42 observations. Unregularised linear learners will be rank deficient here; prefer a tree-based or penalised learner, or pass fewer `stats` to build_features().
#> Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per fold
#> Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per fold
#> Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per fold
#> Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per fold
#> Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per fold
#> Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per fold
#> Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per fold
#> Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per fold
#> Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per fold
#> Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per fold
#> Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per fold
#> Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per fold
#> Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per fold
#> Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per fold
#> Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per fold
#> Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per fold
stack_weights(m)
#>      ranger        enet      cubist         knn 
#> 0.990703715 0.000000000 0.009296285 0.000000000