From e3b3330fc1597280566f7398fa6cca156c977977 Mon Sep 17 00:00:00 2001 From: Yoshihiko Ozaki <30489874+y0z@users.noreply.github.com> Date: Wed, 11 Dec 2024 16:59:50 +0900 Subject: [PATCH] Update package/samplers/smac_sampler/README.md Co-authored-by: Naoto Mizuno --- package/samplers/smac_sampler/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/package/samplers/smac_sampler/README.md b/package/samplers/smac_sampler/README.md index ddc28a26..2df6e98c 100644 --- a/package/samplers/smac_sampler/README.md +++ b/package/samplers/smac_sampler/README.md @@ -25,7 +25,7 @@ Please check the API reference for more details: - `init_design_type`: What initialization sampler to use. Either `"sobol"` (Sobol sequence), `"lhd"` (Latin hypercube), or `"random"`. Default to `"sobol"`. - `surrogate_model_rf_num_trees`: The number of trees used for random forest. Equivalent to `n_estimators` in `RandomForestRegressor` in sklearn. - `surrogate_model_rf_ratio_features`: The ratio of features to use for each tree training in random forest. Equivalent to `max_features` in `RandomForestRegressor` in sklearn. - `surrogate_model_rf_min_samples_split`: The minimum number of samples required to split an internal node: Equivalent to `min_samples_split` in `RandomForestRegressor` in sklearn. +- `surrogate_model_rf_min_samples_split`: The minimum number of samples required to split an internal node: Equivalent to `min_samples_split` in `RandomForestRegressor` in sklearn. `surrogate_model_rf_min_samples_leaf`: The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at least `min_samples_leaf` training samples in each of the left and right branches. This may have the effect of smoothing the model, especially in regression. Equivalent to `min_samples_leaf` in `RandomForestRegressor` in sklearn. `init_design_n_configs`: Number of initial configurations. `init_design_n_configs_per_hyperparameter`: Number of initial configurations per hyperparameter. For example, if my configuration space covers five hyperparameters and `n_configs_per_hyperparameter` is set to 10, then 50 initial configurations will be sampled.