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MiniBatchRegressor

A regressor that 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

MiniBatchRegressor: self

learn_one

Fits to a set of features x and a real-valued target y.

Parameters

  • x (dict)
  • y (numbers.Number)
  • kwargs

Returns

Regressor: 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 outcomes.

predict_one

Predicts the target value of a set of features x.

Parameters

  • x (dict)

Returns

Number: The prediction.