robyn_modelselector: Robyn: Model Selection by Weighted Criteria Scores

In lares: Analytics & Machine Learning Sidekick

Robyn: Model Selection by Weighted Criteria Scores

Description

Consider N best models to select the right ones to study using several criteria/metrics such as potential improvement on budget allocator, how many non-zero coefficients there are, R squared, historical performance, baseline expectation, etc.

Read more about this functionality in Medium post: here.

Usage

robyn_modelselector(
  InputCollect,
  OutputCollect,
  metrics = c("rsq_train", "performance", "potential_improvement", "non_zeroes",
    "incluster_models", "baseline_dist"),
  wt = c(2, 1, 0, 1, 0.1, 0),
  baseline_ref = 0,
  top = 4,
  n_per_cluster = 5,
  allocator_limits = c(0.5, 2),
  quiet = FALSE,
  cache = TRUE,
  ...
)

## S3 method for class '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, "incluster_models" for amount of models per cluster, "baseline_dist" for the difference between the model's baseline and baseline_ref value. You can also use the standard MOO errors: "nrmse", "decomp.rssd", and "mape" (the lowest the error, the highest the score; same for "baseline_dist").
wt	Vector. Weight for each of the normalized metrics selected, to calculate the score and rank models. Must have the same order and length of metrics parameter input.
baseline_ref	Numeric value. Between 0 and 1. What is the baseline percentage you expect? Baseline in this case are all the sales or conversions from non-media channels (organic & paid). Use with "baseline_dist" metric.
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

Value

list with resulting ranked data.frames, weights and plot.

See Also

Other Robyn: robyn_hypsbuilder(), robyn_performance()

lares documentation built on June 22, 2024, 10:27 a.m.