Get hyperparameter values

get_hyperparameter_defaults(models = get_supported_models(), n = 100,
  k = 10, model_class = "classification")

get_random_hyperparameters(models = get_supported_models(), n = 100,
  k = 10, tune_depth = 5, model_class = "classification")

Arguments

models

which algorithms?

n

Number observations

k

Number features

model_class

"classification" or "regression"

tune_depth

How many combinations of hyperparameter values?

Value

Named list of data frames. Each data frame corresponds to an algorithm, and each column in each data fram corresponds to a hyperparameter for that algorithm. This is the same format that should be provided to tune_models(hyperparameters = ) to specify hyperparameter values.

Details

Get hyperparameters for model training. get_hyperparameter_defaults returns a list of 1-row data frames with default hyperparameter values that are used by flash_models. get_random_hyperparameters returns a list of data frames with combinations of random values of hyperparameters to tune over in tune_models; the number of rows in the data frames is given by `tune_depth`.

For get_hyperparameter_defaults k-NN defaults are from the kknn package: kmax = 7, distance = 2 (Minkowski's exponent, i.e. Euclidean distance), kernal = "optimal". Random forest defaults are from Intro to Statistical Learning and caret: mtry = sqrt(k), splitrule = "extratrees", min.node.size = 1 for classification, 5 for regression

See also

models for model and hyperparameter details