# 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.