Build a Deweather Model — build_dw

This function builds a boosted decision tree machine learning model with useful methods for interrogating it in an air quality and meteorological context. Currently, only the xgboost engine is supported.

Usage

build_dw_model(
  data,
  pollutant,
  vars = c("trend", "ws", "wd", "hour", "weekday", "air_temp"),
  tree_depth = 5,
  trees = 200L,
  learn_rate = 0.1,
  mtry = NULL,
  min_n = 10L,
  loss_reduction = 0,
  sample_size = 1L,
  stop_iter = 190L,
  engine = c("xgboost", "lightgbm"),
  ...,
  .date = "date"
)

Arguments

data: An input data.frame containing one pollutant column (defined using pollutant) and a collection of feature columns (defined using vars).
pollutant: The name of the column (likely a pollutant) in data to predict.
vars: The name of the columns in data to use as model features - i.e., to predict the values in the pollutant column. Any character columns will be coerced to factors. "hour", "weekday", "trend", "yday", "week", and "month" are special terms and will be passed to append_dw_vars() if not present in names(data).
tree_depth: An integer for the maximum depth of the tree (i.e. number of splits) (specific engines only).
trees: An integer for the number of trees contained in the ensemble.
learn_rate: A number for the rate at which the boosting algorithm adapts from iteration-to-iteration (specific engines only). This is sometimes referred to as the shrinkage parameter.
mtry: A number for the number (or proportion) of predictors that will be randomly sampled at each split when creating the tree models (specific engines only).
min_n: An integer for the minimum number of data points in a node that is required for the node to be split further.
loss_reduction: A number for the reduction in the loss function required to split further (specific engines only).
sample_size: A number for the number (or proportion) of data that is exposed to the fitting routine. For xgboost, the sampling is done at each iteration while C5.0 samples once during training.
stop_iter: The number of iterations without improvement before stopping (specific engines only).
engine: A single character string specifying what computational engine to use for fitting.
...: Not current used.
.date: The name of the 'date' column which defines the air quality timeseries. Passed to append_dw_vars() if needed. Also used to extract the time zone of the data for later restoration if trend is used as a variable.

Value

a 'Deweather' object for further analysis