Consider N models per cluster to select the right ones to study using several metrics to consider such as potential improvement on budget allocator, how many non-zero coefficients there are, R squared, historical performance, etc.
Usage
robyn_modelselector(
InputCollect,
OutputCollect,
metrics = c("rsq_train", "performance", "potential_improvement", "non_zeroes",
"incluster_models"),
wt = c(2, 1, 1, 1, 0.1),
top = 4,
n_per_cluster = 5,
allocator_limits = c(0.5, 2),
quiet = FALSE,
cache = TRUE,
...
)
# S3 method for robyn_modelselector
plot(x, ...)
Arguments
- InputCollect, OutputCollect
Robyn output objects.
- metrics
Character vector. Which metrics do you want to consider? Pick any combination from: "rsq_train" for trained R squared, "performance" for ROAS or (inverse) CPA, "potential_improvement" for default budget allocator improvement using
allocator_limits
, "non_zeroes" for non-zero beta coefficients, and "incluster_models" for amount of models per cluster.- wt
Vector. Weight for each of the normalized
metrics
selected, to calculate the score and rank models. Must have the same order and length ofmetrics
parameter input.- top
Integer. How many ranked models to star? The better the model is, the more stars it will have marked.
- n_per_cluster
Integer. How many models per cluster do you want to plot? Default: 5. Keep in mind they will all be considered for the calculations.
- allocator_limits
Numeric vector, length 2. How flexible do you want to be with the budget allocator? By default, we'll consider a 0.5X and 2X range to let the budget shift across channels.
- quiet
Boolean. Keep quiet? If not, message will be shown.
- cache
Use cache functionality for allocator's results?
- ...
Additional parameters.
- x
robyn_modelselector object
See also
Other Robyn:
robyn_hypsbuilder()