HoeffdingTreeClassifier¶
Hoeffding Tree or Very Fast Decision Tree classifier.
Parameters¶
-
grace_period
Type → int
Default →
200Number of instances a leaf should observe between split attempts.
-
max_depth
Type → int | None
Default →
NoneThe maximum depth a tree can reach. If
None, the tree will grow indefinitely. -
split_criterion
Type → str
Default →
info_gainSplit criterion to use. - 'gini' - Gini - 'info_gain' - Information Gain - 'hellinger' - Helinger Distance
-
delta
Type → float
Default →
1e-07Significance level to calculate the Hoeffding bound. The significance level is given by
1 - delta. Values closer to zero imply longer split decision delays. -
tau
Type → float
Default →
0.05Threshold below which a split will be forced to break ties.
-
leaf_prediction
Type → str
Default →
nbaPrediction mechanism used at leafs. - 'mc' - Majority Class - 'nb' - Naive Bayes - 'nba' - Naive Bayes Adaptive
-
nb_threshold
Type → int
Default →
0Number of instances a leaf should observe before allowing Naive Bayes.
-
nominal_attributes
Type → list | None
Default →
NoneList of Nominal attributes identifiers. If empty, then assume that all numeric attributes should be treated as continuous.
-
splitter
Type → Splitter | None
Default →
NoneThe 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 propertyis_target_class. This is an advanced option. Special care must be taken when choosing different splitters. By default,tree.splitter.GaussianSplitteris used ifsplitterisNone. -
binary_split
Type → bool
Default →
FalseIf True, only allow binary splits.
-
min_branch_fraction
Type → float
Default →
0.01The minimum percentage of observed data required for branches resulting from split candidates. To validate a split candidate, at least two resulting branches must have a percentage of samples greater than
min_branch_fraction. This criterion prevents unnecessary splits when the majority of instances are concentrated in a single branch. -
max_share_to_split
Type → float
Default →
0.99Only perform a split in a leaf if the proportion of elements in the majority class is smaller than this parameter value. This parameter avoids performing splits when most of the data belongs to a single class.
-
max_size
Type → float
Default →
100.0The max size of the tree, in Megabytes (MB).
-
memory_estimate_period
Type → int
Default →
1000000Interval (number of processed instances) between memory consumption checks.
-
stop_mem_management
Type → bool
Default →
FalseIf True, stop growing as soon as memory limit is hit.
-
remove_poor_attrs
Type → bool
Default →
FalseIf True, disable poor attributes to reduce memory usage.
-
merit_preprune
Type → bool
Default →
TrueIf True, enable merit-based tree pre-pruning.
Attributes¶
-
height
-
leaf_prediction
Return the prediction strategy used by the tree at its leaves.
-
max_size
Max allowed size tree can reach (in MB).
-
n_active_leaves
-
n_branches
-
n_inactive_leaves
-
n_leaves
-
n_nodes
-
split_criterion
Return a string with the name of the split criterion being used by the tree.
-
summary
Collect metrics corresponding to the current status of the tree in a string buffer.
Examples¶
from river.datasets import synth
from river import evaluate
from river import metrics
from river import tree
gen = synth.Agrawal(classification_function=0, seed=42)
dataset = iter(gen.take(1000))
model = tree.HoeffdingTreeClassifier(
grace_period=100,
delta=1e-5,
nominal_attributes=['elevel', 'car', 'zipcode']
)
metric = metrics.Accuracy()
evaluate.progressive_val_score(dataset, model, metric)
Accuracy: 84.58%
Methods¶
debug_one
Print an explanation of how x is predicted.
Parameters
- x — 'dict'
Returns
str | None: A representation of the path followed by the tree to predict x; None if
draw
Draw the tree using the graphviz library.
Since the tree is drawn without passing incoming samples, classification trees will show the majority class in their leaves, whereas regression trees will use the target mean.
Parameters
- max_depth — 'int | None' — defaults to
None
The maximum depth a tree can reach. IfNone, the tree will grow indefinitely.
learn_one
Train the model on instance x and corresponding target y.
Parameters
- x
- y
- w — defaults to
1.0
predict_one
Predict the label of a set of features x.
Parameters
- x — 'dict'
- kwargs
Returns
base.typing.ClfTarget | None: The predicted label.
predict_proba_one
Predict the probability of each label for a dictionary of features x.
Parameters
- x
Returns
A dictionary that associates a probability which each label.
to_dataframe
Return a representation of the current tree structure organized in a pandas.DataFrame object.
In case the tree is empty or it only contains a single node (a leaf), None is returned.
Returns
df
Notes¶
A Hoeffding Tree 1 is an incremental, anytime decision tree induction algorithm that is capable of learning from massive data streams, assuming that the distribution generating examples does not change over time. Hoeffding trees exploit the fact that a small sample can often be enough to choose an optimal splitting attribute. This idea is supported mathematically by the Hoeffding bound, which quantifies the number of observations (in our case, examples) needed to estimate some statistics within a prescribed precision (in our case, the goodness of an attribute).
A theoretically appealing feature of Hoeffding Trees not shared by other incremental decision tree learners is that it has sound guarantees of performance. Using the Hoeffding bound one can show that its output is asymptotically nearly identical to that of a non-incremental learner using infinitely many examples. Implementation based on MOA 2.