`get_hyperparameter_defaults.Rd`

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
(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