Adaptive Random Forest regressor.
The 3 most important aspects of Adaptive Random Forest 1 are:
inducing diversity through re-sampling
inducing diversity through randomly selecting subsets of features for node splits
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 \(\pm3\sigma\).
n_models (int) – defaults to
Number of trees in the ensemble.
max_features – defaults to
Max number of attributes for each node split.
int, then consider
max_featuresat each split.
max_featuresis a percentage and
int(max_features * n_features)features are considered per split.
- If "sqrt", then
- If "log2", then
- If None, then
aggregation_method (str) – defaults to
The method to use to aggregate predictions in the ensemble.
- 'median' - If selected will disable the weighted vote.
lambda_value (int) – defaults to
The lambda value for bagging (lambda=6 corresponds to Leveraging Bagging).
metric (river.metrics.base.RegressionMetric) – defaults to
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.
disable_weighted_vote – defaults to
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
metricvalue to weight predictions.
drift_detector (base.DriftDetector) – defaults to
Drift Detection method. Set to None to disable Drift detection.
warning_detector (base.DriftDetector) – defaults to
Warning Detection method. Set to None to disable warning detection.
grace_period (int) – defaults to
[Tree parameter] Number of instances a leaf should observe between split attempts.
max_depth (int) – defaults to
[Tree parameter] The maximum depth a tree can reach. If
None, the tree will grow indefinitely.
split_confidence (float) – defaults to
[Tree parameter] Allowed error in split decision, a value closer to 0 takes longer to decide.
tie_threshold (float) – defaults to
[Tree parameter] Threshold below which a split will be forced to break ties.
leaf_prediction (str) – defaults to
[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 (base.Regressor) – defaults to
[Tree parameter] The regression model used to provide responses if
leaf_prediction='model'. If not provided, an instance of
river.linear_model.LinearRegressionwith the default hyperparameters is used.
model_selector_decay (float) – defaults to
[Tree parameter] The exponential decaying factor applied to the learning models' squared errors, that are monitored if
leaf_prediction='adaptive'. Must be between
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 (list) – defaults to
[Tree parameter] List of Nominal attributes. If empty, then assume that all attributes are numerical.
splitter (river.tree.splitter.base_splitter.Splitter) – defaults to
[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.splittermodule. 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.EBSTSplitteris used if
min_samples_split (int) – defaults to
[Tree parameter] The minimum number of samples every branch resulting from a split candidate must have to be considered valid.
max_size (int) – defaults to
[Tree parameter] Maximum memory (MB) consumed by the tree.
memory_estimate_period (int) – defaults to
[Tree parameter] Number of instances between memory consumption checks.
seed (int) – defaults to
seedis used to seed the random number generator; If
seedis the random number generator; If
None, the random number generator is the
RandomStateinstance used by
Other parameters passed to
tree.HoeffdingTree. Check the
treemodule documentation for more information.
Valid aggregation_method values.
>>> from river import datasets >>> from river import evaluate >>> from river import metrics >>> from river import ensemble >>> from river import preprocessing >>> dataset = datasets.TrumpApproval() >>> model = ( ... preprocessing.StandardScaler() | ... ensemble.AdaptiveRandomForestRegressor(n_models=3, seed=42) ... ) >>> metric = metrics.MAE() >>> evaluate.progressive_val_score(dataset, model, metric) MAE: 1.870913
Return a fresh estimator with the same parameters.
The clone has the same parameters but has not been updated with any data. This works by looking at the parameters from the class signature. Each parameter is either - recursively cloned if it's a River classes. - deep-copied via
copy.deepcopy if not. If the calling object is stochastic (i.e. it accepts a seed parameter) and has not been seeded, then the clone will not be idempotent. Indeed, this method's purpose if simply to return a new instance with the same input parameters.
Predicts the target value of a set of features
- x (dict)
Number: The prediction.
Reset the forest.
Gomes, H.M., Bifet, A., Read, J., Barddal, J.P., Enembreck, F., Pfharinger, B., Holmes, G. and Abdessalem, T., 2017. Adaptive random forests for evolving data stream classification. Machine Learning, 106(9-10), pp.1469-1495. ↩
Gomes, H.M., Barddal, J.P., Boiko, L.E., Bifet, A., 2018. Adaptive random forests for data stream regression. ESANN 2018. ↩