MiniBatchClassifier¶
A classifier that can can operate on mini-batches.
Methods¶
clone
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.
learn_many
Update the model with a mini-batch of features X
and boolean targets y
.
Parameters
- X (pandas.core.frame.DataFrame)
- y (pandas.core.series.Series)
- kwargs
Returns
MiniBatchClassifier: self
learn_one
Update the model with a set of features x
and a label y
.
Parameters
- x (dict)
- y (Union[bool, str, int])
- kwargs
Returns
Classifier: self
predict_many
Predict the outcome for each given sample.
Parameters --------- X A dataframe of features.
Parameters
- X (pandas.core.frame.DataFrame)
Returns
Series: The predicted labels.
predict_one
Predict the label of a set of features x
.
Parameters
- x (dict)
Returns
typing.Union[bool, str, int]: The predicted label.
predict_proba_many
Predict the outcome probabilities for each given sample.
Parameters
- X (pandas.core.frame.DataFrame)
Returns
DataFrame: A dataframe with probabilities of True
and False
for each sample.
predict_proba_one
Predict the probability of each label for a dictionary of features x
.
Parameters
- x (dict)
Returns
typing.Dict[typing.Union[bool, str, int], float]: A dictionary that associates a probability which each label.