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")
models | which algorithms? |
---|---|
n | Number observations |
k | Number features |
model_class | "classification" or "regression" |
tune_depth | How many combinations of hyperparameter values? |
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.
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
models
for model and hyperparameter details