<commit_before># Copyright (c) 2016, the GPyOpt Authors
# Licensed under the BSD 3-clause license (see LICENSE.txt)

from .base import BOModel
import numpy as np


class RFModel(BOModel):
    """
    General class for handling a Ramdom Forest in GPyOpt.

    .. Note:: The model has beed wrapper 'as it is' from  Scikit-learn. Check
    http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html
    for further details.
    """

    analytical_gradient_prediction = False

    def __init__(self, bootstrap=True, criterion='mse', max_depth=None,
           max_features='auto', max_leaf_nodes=None, min_samples_leaf=1,
           min_samples_split=2, min_weight_fraction_leaf=0.0,
           n_estimators=500, n_jobs=1, oob_score=False, random_state=None,
           verbose=0, warm_start=False):

        self.bootstrap = bootstrap
        self.criterion = criterion<commit_msg>Correct typo in RFModel docstring<commit_after># Copyright (c) 2016, the GPyOpt Authors
# Licensed under the BSD 3-clause license (see LICENSE.txt)

from .base import BOModel
import numpy as np


class RFModel(BOModel):
    """
    General class for handling a Random Forest in GPyOpt.

    .. Note:: The model has beed wrapper 'as it is' from  Scikit-learn. Check
    http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html
    for further details.
    """

    analytical_gradient_prediction = False

    def __init__(self, bootstrap=True, criterion='mse', max_depth=None,
           max_features='auto', max_leaf_nodes=None, min_samples_leaf=1,
           min_samples_split=2, min_weight_fraction_leaf=0.0,
           n_estimators=500, n_jobs=1, oob_score=False, random_state=None,
           verbose=0, warm_start=False):

        self.bootstrap = bootstrap
        self.criterion = criterion
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{'dir': 'git-commits-cleaned', 'id': '6239707', 'n_tokens_mistral': 612, 'n_tokens_neox': 568, 'n_words': 147}