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"
)
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.
which algorithms?
Number observations
Number features
"classification" or "regression"
How many combinations of 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