Source code for h2o.estimators.random_forest

from .estimator_base import *

[docs]class H2ORandomForestEstimator(H2OEstimator): """Builds a Random Forest Model on an H2OFrame Parameters ---------- model_id : str, optional The unique id assigned to the resulting model. If none is given, an id will automatically be generated. mtries : int Number of variables randomly sampled as candidates at each split. If set to -1, defaults to sqrt{p} for classification, and p/3 for regression, where p is the number of predictors. sample_rate : float Row sample rate (from 0.0 to 1.0) col_sample_rate_per_tree : float Column sample rate per tree (from 0.0 to 1.0) build_tree_one_node : bool Run on one node only; no network overhead but fewer CPUs used. Suitable for small datasets. ntrees : int A non-negative integer that determines the number of trees to grow. max_depth : int Maximum depth to grow the tree. min_rows : int Minimum number of rows to assign to terminal nodes. nbins : int For numerical columns (real/int), build a histogram of (at least) this many bins, then split at the best point. nbins_top_level : int For numerical columns (real/int), build a histogram of (at most) this many bins at the root level, then decrease by factor of two per level. nbins_cats : int For categorical columns (factors), build a histogram of this many bins, then split at the best point. Higher values can lead to more overfitting. binomial_double_trees : bool or binary classification: Build 2x as many trees (one per class) - can lead to higher accuracy. balance_classes : bool logical, indicates whether or not to balance training data class counts via over/under-sampling (for imbalanced data) max_after_balance_size : float Maximum relative size of the training data after balancing class counts (can be less than 1.0). Ignored if balance_classes is False, which is the default behavior. seed : int Seed for random numbers (affects sampling) - Note: only reproducible when running single threaded nfolds : int, optional Number of folds for cross-validation. If nfolds >= 2, then validation must remain empty. fold_assignment : str Cross-validation fold assignment scheme, if fold_column is not specified Must be "AUTO", "Random" or "Modulo" keep_cross_validation_predictions : bool Whether to keep the predictions of the cross-validation models score_each_iteration : bool Attempts to score each tree. score_tree_interval : int Score the model after every so many trees. Disabled if set to 0. stopping_rounds : int Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve (by stopping_tolerance) for k=stopping_rounds scoring events. Can only trigger after at least 2k scoring events. Use 0 to disable. stopping_metric : str Metric to use for convergence checking, only for _stopping_rounds > 0 Can be one of "AUTO", "deviance", "logloss", "MSE", "AUC", "r2", "misclassification". stopping_tolerance : float Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) """ def __init__(self, model_id=None, mtries=None, sample_rate=None, col_sample_rate_per_tree=None, build_tree_one_node=None, ntrees=None, max_depth=None, min_rows=None, nbins=None, nbins_cats=None, binomial_double_trees=None, balance_classes=None, max_after_balance_size=None, seed=None, nfolds=None, fold_assignment=None, stopping_rounds=None, stopping_metric=None, stopping_tolerance=None, score_each_iteration=None, score_tree_interval=None, keep_cross_validation_predictions=None, checkpoint=None): super(H2ORandomForestEstimator, self).__init__() self._parms = locals() self._parms = {k:v for k,v in self._parms.items() if k!="self"} @property def mtries(self): return self._parms["mtries"] @mtries.setter def mtries(self, value): self._parms["mtries"] = value @property def sample_rate(self): return self._parms["sample_rate"] @sample_rate.setter def sample_rate(self, value): self._parms["sample_rate"] = value @property def col_sample_rate_per_tree(self): return self._parms["col_sample_rate_per_tree"] @col_sample_rate_per_tree.setter def col_sample_rate_per_tree(self, value): self._parms["col_sample_rate_per_tree"] = value @property def build_tree_one_node(self): return self._parms["build_tree_one_node"] @build_tree_one_node.setter def build_tree_one_node(self, value): self._parms["build_tree_one_node"] = value @property def ntrees(self): return self._parms["ntrees"] @ntrees.setter def ntrees(self, value): self._parms["ntrees"] = value @property def max_depth(self): return self._parms["max_depth"] @max_depth.setter def max_depth(self, value): self._parms["max_depth"] = value @property def min_rows(self): return self._parms["min_rows"] @min_rows.setter def min_rows(self, value): self._parms["min_rows"] = value @property def nbins(self): return self._parms["nbins"] @nbins.setter def nbins(self, value): self._parms["nbins"] = value @property def nbins_cats(self): return self._parms["nbins_cats"] @nbins_cats.setter def nbins_cats(self, value): self._parms["nbins_cats"] = value @property def binomial_double_trees(self): return self._parms["binomial_double_trees"] @binomial_double_trees.setter def binomial_double_trees(self, value): self._parms["binomial_double_trees"] = value @property def balance_classes(self): return self._parms["balance_classes"] @balance_classes.setter def balance_classes(self, value): self._parms["balance_classes"] = value @property def max_after_balance_size(self): return self._parms["max_after_balance_size"] @max_after_balance_size.setter def max_after_balance_size(self, value): self._parms["max_after_balance_size"] = value @property def seed(self): return self._parms["seed"] @seed.setter def seed(self, value): self._parms["seed"] = value @property def nfolds(self): return self._parms["nfolds"] @nfolds.setter def nfolds(self, value): self._parms["nfolds"] = value @property def fold_assignment(self): return self._parms["fold_assignment"] @fold_assignment.setter def fold_assignment(self, value): self._parms["fold_assignment"] = value @property def keep_cross_validation_predictions(self): return self._parms["keep_cross_validation_predictions"] @keep_cross_validation_predictions.setter def keep_cross_validation_predictions(self, value): self._parms["keep_cross_validation_predictions"] = value @property def score_each_iteration(self): return self._parms["score_each_iteration"] @score_each_iteration.setter def score_each_iteration(self, value): self._parms["score_each_iteration"] = value @property def score_tree_interval(self): return self._parms["score_tree_interval"] @score_tree_interval.setter def score_tree_interval(self, value): self._parms["score_tree_interval"] = value @property def stopping_rounds(self): return self._parms["stopping_rounds"] @stopping_rounds.setter def stopping_rounds(self, value): self._parms["stopping_rounds"] = value @property def stopping_metric(self): return self._parms["stopping_metric"] @stopping_metric.setter def stopping_metric(self, value): self._parms["stopping_metric"] = value @property def stopping_tolerance(self): return self._parms["stopping_tolerance"] @stopping_tolerance.setter def stopping_tolerance(self, value): self._parms["stopping_tolerance"] = value @property def checkpoint(self): return self._parms["checkpoint"] @checkpoint.setter def checkpoint(self, value): self._parms["checkpoint"] = value