ModelSelectionClassifier¶
A model selector for classification.
Parameters¶
-
models (Iterator[base.Estimator])
-
metric (river.metrics.base.Metric)
Attributes¶
-
best_model
The current best model.
-
models
Methods¶
append
S.append(value) -- append value to the end of the sequence
Parameters
- item
clear
S.clear() -> None -- remove all items from S
copy
count
S.count(value) -> integer -- return number of occurrences of value
Parameters
- item
extend
S.extend(iterable) -- extend sequence by appending elements from the iterable
Parameters
- other
index
S.index(value, [start, [stop]]) -> integer -- return first index of value. Raises ValueError if the value is not present.
Supporting start and stop arguments is optional, but recommended.
Parameters
- item
- args
insert
S.insert(index, value) -- insert value before index
Parameters
- i
- item
learn_one
Update the model with a set of features x and a label y.
Parameters
- x (dict)
- y (Union[bool, str, int])
Returns
Classifier: self
pop
S.pop([index]) -> item -- remove and return item at index (default last). Raise IndexError if list is empty or index is out of range.
Parameters
- i – defaults to
-1
predict_one
Predict the label of a set of features x.
Parameters
- x (dict)
- kwargs
Returns
typing.Union[bool, str, int, NoneType]: 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.
remove
S.remove(value) -- remove first occurrence of value. Raise ValueError if the value is not present.
Parameters
- item
reverse
S.reverse() -- reverse IN PLACE