code: def __init__(self, params):
      super(RandomForestAlgorithm, self).__init__(params)
      logger.debug("RandomForestAlgorithm.__init__")
  
      self.library_version = sklearn.__version__
      self.trees_in_step = additional.get("trees_in_step", 5)
      self.max_steps = additional.get("max_steps", 3)
      self.early_stopping_rounds = additional.get(
          "early_stopping_rounds", 50
      )
      self.model = RandomForestClassifier(
          n_estimators=self.trees_in_step,
          criterion=params.get("criterion", "gini"),
          max_features=params.get("max_features", 0.8),
          max_depth=params.get("max_depth", 6),
          min_samples_split=params.get("min_samples_split", 4),
          min_samples_leaf=params.get("min_samples_leaf", 1),
          warm_start=True,
          n_jobs=params.get("n_jobs", -1),
          random_state=params.get("seed", 1),
      )
      self.max_steps = self.params.get("max_steps", self.max_steps)
  
file_path: mljar-supervised/supervised/algorithms/random_forest.py
imports: import logging
  import os
  import numpy
  import sklearn
  from sklearn.base import ClassifierMixin, RegressorMixin
  from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
  from supervised.algorithms.algorithm import BaseAlgorithm
  from supervised.algorithms.registry import BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, REGRESSION, AlgorithmsRegistry
  from supervised.algorithms.sklearn import SklearnTreesEnsembleClassifierAlgorithm, SklearnTreesEnsembleRegressorAlgorithm
  from supervised.utils.config import LOG_LEVEL
label_desc: Use image node_id 1 to create a new RandomForestAlgorithm object from inherited base classes: SklearnTreesEnsembleClassifierAlgorithm, ClassifierMixin with example: obj = RandomForestAlgorithm(params)