encode_factors_as_numeric: Encode factor data as a single numeric feature, via Bayesian...

In abrahamalex13/doPrepExplore: Standard Data Preparation/Exploration Workflows

Description

Objective: given a factor level, derive an implied y-value. Utilize a 'y-aware' derivation method. Given a factor level, what does its Y vector look like? Then consider an instance of factor level j in rows 1:k. To derive an implied y-value here, don't use those instances in training. Use other available instances, in cross-validated fashion.

Usage

derive_posteriors_p_summaries_supervised_factor(
  df,
  n_splits = 3,
  varname_factor,
  par_prior_beta,
  Y,
  directory_operators
)

join_posteriors_p_summaries_supervised_factor(
  df,
  varname_factor,
  run_type,
  directory_operators
)

transform_posteriors_p_summaries_supervised_factor(
  df,
  varname_factor,
  run_type,
  par_prior_beta,
  Y,
  directory_operators
)

Details

Given a factor level (and if 'train'-type execution, data split ID), join implied-y value stored in reference operator.

Execute connected sequence for encoding factor levels using implied y values.

See Also

Feature Engineering and Selection: A Practical Approach for Predictive Models; Max Kuhn, Kjell Johnson.

abrahamalex13/doPrepExplore documentation built on Jan. 27, 2021, 4:30 a.m.