SoftmaxRegression

Softmax regression is a generalization of logistic regression to multiple classes.

Softmax regression is also known as "multinomial logistic regression". There are a set weights for each class, hence the weights attribute is a nested collections.defaultdict. The main advantage of using this instead of a one-vs-all logistic regression is that the probabilities will be calibrated. Moreover softmax regression is more robust to outliers.

Parameters

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

  The sequential optimizer used to tune the weights.

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

  The loss function to optimize for.

• l2 – defaults to 0

  Amount of L2 regularization used to push weights towards 0.

Attributes

• weights (collections.defaultdict)

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.ImageSegments()

>>> model = preprocessing.StandardScaler()
>>> model |= linear_model.SoftmaxRegression()

>>> metric = metrics.MacroF1()

>>> evaluate.progressive_val_score(dataset, model, metric)
MacroF1: 81.88%

Methods

learn_one

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

Parameters

• x (dict)
• y (Union[bool, str, int])

Returns

Classifier: self

predict_one

Predict the label of a set of features x.

Parameters

• x (dict)

Returns

typing.Union[bool, str, int, NoneType]: The predicted label.

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.

References

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1. Course on classification stochastic gradient descent ↩

2. Binary vs. Multi-Class Logistic Regression ↩