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")
which algorithms?
Number observations
Number features
"classification" or "regression"
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
(except for glm, which is a 10-row data frame) 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
XGBoost defaults are from caret and XGBoost documentation:
eta = 0.3, gamma = 0, max_depth = 6, subsample = 0.7,
colsample_bytree = 0.8, min_child_weight = 1, and nrounds = 50.
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.
glm defaults are
from caret: alpha = 1, and because glmnet fits sequences of lambda nearly as
fast as an individual value, lambda is a sequence from 1e-4 to 8.
models for model and hyperparameter details