``prior``
---------

- Available in: GLM
- Hyperparameter: no

Description
~~~~~~~~~~~

This option specifies the prior probability of class 1 in the response when ``family = "binomial"``.  The default value is the observation frequency of class 1. This must be a value from (0,1) exclusive range, and defaults to -1 (no prior). This parameter is useful for logistic regression if the data has been sampled and the mean of response does not reflect reality. 

Related Parameters
~~~~~~~~~~~~~~~~~~

- None

Example
~~~~~~~

.. example-code::
   .. code-block:: r

    library(h2o)
    h2o.init()

    # import the cars dataset:
    # this dataset is used to classify whether or not a car is economical based on
    # the car's displacement, power, weight, and acceleration, and the year it was made
    cars <- h2o.importFile("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")

    # convert response column to a factor
    cars["economy_20mpg"] <- as.factor(cars["economy_20mpg"])

    # set the predictor names and the response column name
    predictors <- c("displacement","power","weight","acceleration","year")
    response <- "economy_20mpg"

    # split into train and validation
    cars.splits <- h2o.splitFrame(data = cars, ratios = .8)
    train <- cars.splits[[1]]
    valid <- cars.splits[[2]]

    # Build a GLM model and set a prior value of 0.5
    car_glm1 <- h2o.glm(x = predictors, y = response, family = 'binomial', prior=0.5,
                        training_frame = train, 
                        validation_frame = valid)

    # Build a GLM model without a prior value
    car_glm2 <- h2o.glm(x = predictors, y = response, family = 'binomial',
                        training_frame = train,
                        validation_frame = valid)

    # Check the coefficients for both models
    car_glm1@model$coefficients_table
    car_glm2@model$coefficients_table

   .. code-block:: python

    import h2o
    from h2o.estimators.glm import H2OGeneralizedLinearEstimator
    h2o.init()

    # import the cars dataset:
    # this dataset is used to classify whether or not a car is economical based on
    # the car's displacement, power, weight, and acceleration, and the year it was made
    cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")

    # convert response column to a factor
    cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()

    # set the predictor names and the response column name
    predictors = ["displacement","power","weight","acceleration","year"]
    response = "economy_20mpg"

    # split into train and validation sets
    train, valid = cars.split_frame(ratios = [.8])

    # Build a GLM model and set a prior value of 0.5
    cars_glm1 = H2OGeneralizedLinearEstimator(family = 'binomial', prior=0.5)
    cars_glm1.train(x = predictors, y = response, 
                    training_frame = train, 
                    validation_frame = valid)

    # Build a GLM model and set a prior value of 0.5
    cars_glm2 = H2OGeneralizedLinearEstimator(family = 'binomial')
    cars_glm2.train(x = predictors, y = response, 
                    training_frame = train, 
                    validation_frame = valid)

    # Check the coefficients for both models
    coeff_table1 = cars_glm1._model_json['output']['coefficients_table']
    coeff_table1

    coeff_table2 = cars_glm2._model_json['output']['coefficients_table']
    coeff_table2