LogisticRegression

Logistic 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.

Parameters

• optimizer

  Type → optim.base.Optimizer | None

  Default → None

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

• loss

  Type → optim.losses.BinaryLoss | None

  Default → None

  The loss function to optimize for. Defaults to optim.losses.Log.

• l2

  Default → 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

  Default → 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

  Default → 0.0

  Initial intercept value.

• intercept_lr

  Type → float | optim.base.Scheduler

  Default → 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

  Default → 1000000000000.0

  Clips the absolute value of each gradient value.

• initializer

  Type → optim.base.Initializer | None

  Default → None

  Weights initialization scheme.

Attributes

• weights

  The current weights.

Examples

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

dataset = datasets.Phishing()

model = (
    preprocessing.StandardScaler() |
    linear_model.LogisticRegression(optimizer=optim.SGD(.1))
)

metric = metrics.Accuracy()

evaluate.progressive_val_score(dataset, model, metric)

Accuracy: 88.96%

Methods

learn_many

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

Parameters

• X — 'pd.DataFrame'
• y — 'pd.Series'
• w — 'float | pd.Series' — defaults to 1

learn_one

Update the model with a set of features x and a label y.

Parameters

• x — 'dict'
• y — 'base.typing.ClfTarget'
• w — defaults to 1.0

predict_many

Predict the outcome for each given sample.

Parameters

• X — 'pd.DataFrame'

Returns

pd.Series: The predicted labels.

predict_one

Predict the label of a set of features x.

Parameters

• x — 'dict'
• kwargs

Returns

base.typing.ClfTarget | None: The predicted label.

predict_proba_many

Predict the outcome probabilities for each given sample.

Parameters

• X — 'pd.DataFrame'

Returns

pd.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

Returns

A dictionary that associates a probability which each label.