Make Predictions from a "NaiveBayes" Object

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

# S3 method for NaiveBayes
predict(
  model,
  data,
  key,
  features = NULL,
  alpha = NULL,
  verbose = NULL,
  thread.ratio = NULL
)

Arguments

model

R6Class object
A "NaiveBayes" object for prediction.
data

DataFrame
DataFrame containting the data.
key

character
Name of the ID column.
features

character of list of characters, optional
Name of feature columns for prediction.
If not provided, it defaults to all non-key columns of data.
alpha

double, optional
Laplace smoothing value. Set a positive value to enable Laplace smoothing for categorical variables and use that value as the smoothing parameter. Set value 0 to disable Laplace smoothing.
Defaults to the alpha value in the JSON model, if there is one, or 0 otherwise.
verbose

logical, optional
If TRUE, output all classes and the corresponding confidences for each data point.
Defaults to FALSE.
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.

Format

S3 methods

Value

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

  • ID - with the same name and type as data's ID column.

  • CLASS - predicted class name

  • CONFIDENCE - confidence for the prediction of the sample, which is a logarithmic value of the posterior probabilities.

Note

A non-zero Laplace value (alpha) is required if there exist discrete category values that only occur in the test set. It can be read from JSON models or from the parameter alpha in predict(). The Laplace value you set here takes precedence over the values read from JSON models.

Examples

Input DataFrame df2 for prediction:

> df2$Collect()
   ID HOMEOWNER MARITALSTATUS  ANNUALINCOME
1   0        NO       Married         120.0
2   1       YES       Married         180.0
3   2        NO        Single          90.0

Call the function and predict with a "NaiveBayes" object nb:

> predict(nb, df2, "ID", alpha=1.0, verbose=TRUE)
   ID CLASS  CONFIDENCE
1   0    NO   -6.572353
2   0   YES  -23.747252
3   1    NO   -7.602221
4   1   YES -169.133547
5   2    NO   -7.133599
6   2   YES   -4.648640

See also

hanaml.NaiveBayes