随机森林叶节点加权python代码

感谢您的提问。以下是我给您找到的随机森林叶节点加权的 Python 代码：

from sklearn.ensemble import RandomForestClassifier

class WeightedRandomForestClassifier(RandomForestClassifier):
  def __init__(self, n_estimators=100, criterion='gini', max_depth=None,
               min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0,
               max_features='auto', max_leaf_nodes=None, bootstrap=True,
               oob_score=False, n_jobs=None, random_state=None, verbose=0,
               class_weight=None, ccp_alpha=0.0, min_impurity_decrease=0.0,
               min_impurity_split=None, warm_start=False):
    super().__init__(n_estimators=n_estimators, criterion=criterion, max_depth=max_depth,
                     min_samples_split=min_samples_split, min_samples_leaf=min_samples_leaf,
                     min_weight_fraction_leaf=min_weight_fraction_leaf, max_features=max_features,
                     max_leaf_nodes=max_leaf_nodes, bootstrap=bootstrap,
                     oob_score=oob_score, n_jobs=n_jobs, random_state=random_state, verbose=verbose,
                     class_weight=class_weight, ccp_alpha=ccp_alpha, min_impurity_decrease=min_impurity_decrease,
                     min_impurity_split=min_impurity_split, warm_start=warm_start)

  def fit(self, X, y, sample_weight=None):
    self.sample_weight = sample_weight
    super().fit(X, y)

  def _make_estimator(self, append=True):
    estimator = super()._make_estimator(append=append)

    if self.sample_weight is not None:
      estimator.tree_.value *= self.sample_weight[:, np.newaxis]

    return estimator

此处提供的代码是基于 sklearn 的随机森林实现的，具体做法是重载了 `fit` 方法，将外部传入的随机森林叶节点的权重保存到实例的 `sample_weight` 属性中，然后在叶节点进行加权（通过与叶子节点的值相乘的方式）。注意，在使用此方法时，需要对样本的权重进行特殊处理。