Description Usage Arguments Details Value See Also

View source: R/setup_hyperparameters.R

Get hyperparameter values

1 2 3 4 5 | ```
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? |

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.

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.

`models`

for model and hyperparameter details

healthcareai documentation built on Dec. 13, 2018, 1:04 a.m.

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