Make Predictions from a

Similar to other predict methods, this function predicts fitted values from a fitted "PolynomialRegression" object.

# S3 method for PolynomialRegression
predict(
  model,
  data,
  key,
  features = NULL,
  thread.ratio = NULL,
  model.format = NULL
)

Arguments

model	`R6Class object` A "PolynomialRegression" object for prediction.
data	`DataFrame` DataFrame containting the data.
key	`character` Name of the ID column.
features	`character, optional` Name of feature column for prediciton. If not provided, it defaults to the first non-key column of data.
thread.ratio	`double, optional` Controls the proportion of available threads that can be used by this function. The value range is from 0 to 1, where 0 indicates a single thread, and 1 indicates all available threads. Values between 0 and 1 will use up to that percentage of available threads.Values outside this range are ignored. Defaults to 0.
model.format	`character, optional` Choose the format of mdoel for prediction. optional are "coefficients" or "pmml". Defaults to "coefficients".

S3 methods

Predicted values are returned as a DataFrame, structured as follows.

Input DataFrame data2:

> data2$Collect()
  ID   X1
1  0 0.30
2  1 4.00
3  2 1.60
4  3 0.45
5  4 1.70

Predict with DataFrame data2 and the "PolynomialRegression" Object pr:

> predict(pr, data2, key = "ID")
   ID      VALUE
0   1   6.157063
1   2   8.401269
2   3  15.668581
3   4  33.928501