spark_pipeline_stage

Create a Pipeline Stage Object

Description

Helper function to create pipeline stage objects with common parameter setters.

Usage

spark_pipeline_stage(
  sc,
  class,
  uid,
  features_col = NULL,
  label_col = NULL,
  prediction_col = NULL,
  probability_col = NULL,
  raw_prediction_col = NULL,
  k = NULL,
  max_iter = NULL,
  seed = NULL,
  input_col = NULL,
  input_cols = NULL,
  output_col = NULL,
  output_cols = NULL
)

Arguments

Argument Description
sc A spark_connection object.
class Class name for the pipeline stage.
uid A character string used to uniquely identify the ML estimator.
features_col Features column name, as a length-one character vector. The column should be single vector column of numeric values. Usually this column is output by ft_r_formula.
label_col Label column name. The column should be a numeric column. Usually this column is output by ft_r_formula.
prediction_col Prediction column name.
probability_col Column name for predicted class conditional probabilities.
raw_prediction_col Raw prediction (a.k.a. confidence) column name.
k The number of clusters to create
max_iter The maximum number of iterations to use.
seed A random seed. Set this value if you need your results to be reproducible across repeated calls.
input_col The name of the input column.
input_cols Names of output columns.
output_col The name of the output column.
thresholds Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values > 0 excepting that at most one value may be 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class’s threshold.