simple_workflow: A simple learning and prediction workflow

In mrfoliveira/STResampling-DSAA2019: Biased Resampling Strategies for Imbalanced Spatio-Temporal Forecasting

Description

A simple learning and prediction workflow that may deal with NAs and use re-sampling techniques to balance an imbalanced regression problem.

Usage

simple_workflow(train, test, form, model = "lm", resample = NULL,
  resample.pars = NULL, handleNAs = NULL, min_train = 2,
  nORp = 0.2, time = "time", site_id = "site", ...)

Arguments

train	a data frame for training
test	a data frame for testing
form	a formula describing the model to learn
model	the name of the algorithm to use
resample	re-sampling technique to be used. Default is NULL.
resample.pars	parameters to be passed to re-sample function. Default is NULL.
handleNAs	string indicating how to deal with NAs. If "centralImput", training observations with at least 80% of non-NA columns, will have their NAs substituted by the mean value and testing observatiosn will have their NAs filled in with mean value regardless. Default is NULL.
min_train	a minimum number of observations that must be left to train a model. If there are not enough observations, predictions will be NA. Default is 2.
nORp	a maximum number or fraction of columns/rows with missing values above which a row/column will be removed from train before learning the model. Only works if handleNAs was set to centralImputation. Default is 0.2.
time	the name of the column in train and test containing time-stamps
site_id	the name of the column in train and test containing location IDs
...	other parameters to feed to model

Value

a data frame containing time-stamps, location IDs, true values and predicted values

mrfoliveira/STResampling-DSAA2019 documentation built on April 9, 2021, 5:39 a.m.