ARFRegressor¶

Adaptive Random Forest regressor.

The 3 most important aspects of Adaptive Random Forest ¹ are:

1. inducing diversity through re-sampling

2. inducing diversity through randomly selecting subsets of features for node splits

3. drift detectors per base tree, which cause selective resets in response to drifts

Notice that this implementation is slightly different from the original algorithm proposed in ². The HoeffdingTreeRegressor is used as base learner, instead of FIMT-DD. It also adds a new strategy to monitor the predictions and check for concept drifts. The deviations of the predictions to the target are monitored and normalized in the [0, 1] range to fulfill ADWIN's requirements. We assume that the data subjected to the normalization follows a normal distribution, and thus, lies within the interval of the mean \(\pm3\sigma\).

Parameters¶

• n_models

  Type → int

  Default → 10

  Number of trees in the ensemble.

• max_features (mutable)

  Default → sqrt

  Max number of attributes for each node split.
  - If int, then consider max_features at each split.
  - If float, then max_features is a percentage and int(max_features * n_features) features are considered per split.
  - If "sqrt", then max_features=sqrt(n_features).
  - If "log2", then max_features=log2(n_features).
  - If None, then max_features=n_features.

• aggregation_method (mutable)

  Type → str

  Default → median

  The method to use to aggregate predictions in the ensemble.
  - 'mean'
  - 'median' - If selected will disable the weighted vote.

• lambda_value (mutable)

  Type → int

  Default → 6

  The lambda value for bagging (lambda=6 corresponds to Leveraging Bagging).

• metric

  Type → metrics.base.RegressionMetric | None

  Default → None

  Metric used to track trees performance within the ensemble. Depending, on the configuration, this metric is also used to weight predictions from the members of the ensemble. Defaults to metrics.MSE().

• disable_weighted_vote

  Default → True

  If True, disables the weighted vote prediction, i.e. does not assign weights to individual tree's predictions and uses the arithmetic mean instead. Otherwise will use the metric value to weight predictions.

• drift_detector

  Type → base.DriftDetector | None

  Default → None

  Drift Detection method. Set to None to disable Drift detection. Defaults to drift.ADWIN(0.001).

• warning_detector

  Type → base.DriftDetector | None

  Default → None

  Warning Detection method. Set to None to disable warning detection. Defaults to drift.ADWIN(0.01).

• grace_period (mutable)

  Type → int

  Default → 50

  [Tree parameter] Number of instances a leaf should observe between split attempts.

• max_depth

  Type → int | None

  Default → None

  [Tree parameter] The maximum depth a tree can reach. If None, the tree will grow until the system recursion limit.

• delta (mutable)

  Type → float

  Default → 0.01

  [Tree parameter] Allowed error in split decision, a value closer to 0 takes longer to decide.

• tau (mutable)

  Type → float

  Default → 0.05

  [Tree parameter] Threshold below which a split will be forced to break ties.

• leaf_prediction

  Type → str

  Default → adaptive

  [Tree parameter] Prediction mechanism used at leaves.
  - 'mean' - Target mean
  - 'model' - Uses the model defined in leaf_model
  - 'adaptive' - Chooses between 'mean' and 'model' dynamically
  

• leaf_model

  Type → base.Regressor | None

  Default → None

  [Tree parameter] The regression model used to provide responses if leaf_prediction='model'. If not provided, an instance of river.linear_model.LinearRegression with the default hyperparameters is used.

• model_selector_decay (mutable)

  Type → float

  Default → 0.95

  [Tree parameter] The exponential decaying factor applied to the learning models' squared errors, that are monitored if leaf_prediction='adaptive'. Must be between 0 and 1. The closer to 1, the more importance is going to be given to past observations. On the other hand, if its value approaches 0, the recent observed errors are going to have more influence on the final decision.

• nominal_attributes

  Type → list | None

  Default → None

  [Tree parameter] List of Nominal attributes. If empty, then assume that all attributes are numerical.

• splitter

  Type → Splitter | None

  Default → None

  [Tree parameter] The Splitter or Attribute Observer (AO) used to monitor the class statistics of numeric features and perform splits. Splitters are available in the tree.splitter module. Different splitters are available for classification and regression tasks. Classification and regression splitters can be distinguished by their property is_target_class. This is an advanced option. Special care must be taken when choosing different splitters.By default, tree.splitter.EBSTSplitter is used if splitter is None.

• min_samples_split

  Type → int

  Default → 5

  [Tree parameter] The minimum number of samples every branch resulting from a split candidate must have to be considered valid.

• binary_split

  Type → bool

  Default → False

  [Tree parameter] If True, only allow binary splits.

• max_size

  Type → float

  Default → 500.0

  [Tree parameter] Maximum memory (MiB) consumed by the tree.

• memory_estimate_period

  Type → int

  Default → 2000000

  [Tree parameter] Number of instances between memory consumption checks.

• stop_mem_management

  Type → bool

  Default → False

  [Tree parameter] If True, stop growing as soon as memory limit is hit.

• remove_poor_attrs

  Type → bool

  Default → False

  [Tree parameter] If True, disable poor attributes to reduce memory usage.

• merit_preprune

  Type → bool

  Default → True

  [Tree parameter] If True, enable merit-based tree pre-pruning.

• seed

  Type → int | None

  Default → None

  Random seed for reproducibility.

Attributes¶

• models

• valid_aggregation_method

  Valid aggregation_method values.

Examples¶

from river import datasets
from river import evaluate
from river import forest
from river import metrics
from river import preprocessing

dataset = datasets.TrumpApproval()

model = (
    preprocessing.StandardScaler() |
    forest.ARFRegressor(seed=42)
)

metric = metrics.MAE()

evaluate.progressive_val_score(dataset, model, metric)

MAE: 0.772113

Methods¶