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Linear regression.

This estimator supports learning with mini-batches. On top of the single instance methods, it provides the following methods: learn_many, predict_many, predict_proba_many. Each method takes as input a pandas.DataFrame where each column represents a feature.

It is generally a good idea to scale the data beforehand in order for the optimizer to converge. You can do this online with a preprocessing.StandardScaler.


  • optimizer (optim.base.Optimizer) – defaults to None

    The sequential optimizer used for updating the weights. Note that the intercept updates are handled separately.

  • loss (optim.losses.RegressionLoss) – defaults to None

    The loss function to optimize for.

  • l2 – defaults to 0.0

    Amount of L2 regularization used to push weights towards 0. For now, only one type of penalty can be used. The joint use of L1 and L2 is not explicitly supported.

  • l1 – defaults to 0.0

    Amount of L1 regularization used to push weights towards 0. For now, only one type of penalty can be used. The joint use of L1 and L2 is not explicitly supported.

  • intercept_init – defaults to 0.0

    Initial intercept value.

  • intercept_lr (Union[optim.base.Scheduler, float]) – defaults to 0.01

    Learning rate scheduler used for updating the intercept. A optim.schedulers.Constant is used if a float is provided. The intercept is not updated when this is set to 0.

  • clip_gradient – defaults to 1000000000000.0

    Clips the absolute value of each gradient value.

  • initializer (optim.base.Initializer) – defaults to None

    Weights initialization scheme.


  • weights (dict)

    The current weights.


>>> from river import datasets
>>> from river import evaluate
>>> from river import linear_model
>>> from river import metrics
>>> from river import preprocessing

>>> dataset = datasets.TrumpApproval()

>>> model = (
...     preprocessing.StandardScaler() |
...     linear_model.LinearRegression(intercept_lr=.1)
... )
>>> metric = metrics.MAE()

>>> evaluate.progressive_val_score(dataset, model, metric)
MAE: 0.555971

>>> model['LinearRegression'].intercept

You can call the debug_one method to break down a prediction. This works even if the linear regression is part of a pipeline.

>>> x, y = next(iter(dataset))
>>> report = model.debug_one(x)
>>> print(report)
0. Input
gallup: 43.84321 (float)
ipsos: 46.19925 (float)
morning_consult: 48.31875 (float)
ordinal_date: 736389 (int)
rasmussen: 44.10469 (float)
you_gov: 43.63691 (float)
1. StandardScaler
gallup: 1.18810 (float)
ipsos: 2.10348 (float)
morning_consult: 2.73545 (float)
ordinal_date: -1.73032 (float)
rasmussen: 1.26872 (float)
you_gov: 1.48391 (float)
2. LinearRegression
Name              Value      Weight      Contribution
      Intercept    1.00000    35.61767       35.61767
          ipsos    2.10348     0.62689        1.31866
morning_consult    2.73545     0.24180        0.66144
         gallup    1.18810     0.43568        0.51764
      rasmussen    1.26872     0.28118        0.35674
        you_gov    1.48391     0.03123        0.04634
   ordinal_date   -1.73032     3.45162       -5.97242
Prediction: 32.54607



Debugs the output of the linear regression.


  • x (dict)
  • decimals (int) – defaults to 5


str: A table which explains the output.


Update the model with a mini-batch of features X and real-valued targets y.


  • X (pandas.core.frame.DataFrame)
  • y (pandas.core.series.Series)
  • w (Union[float, pandas.core.series.Series]) – defaults to 1


MiniBatchRegressor: self


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


  • x (dict)
  • y (numbers.Number)
  • w – defaults to 1.0


Regressor: self


Predict the outcome for each given sample.


  • X


The predicted outcomes.


Predict the output of features x.


  • x


The prediction.