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
NoneThe sequential optimizer used to tune the weights.
-
loss (optim.losses.MultiClassLoss) – defaults to
NoneThe loss function to optimize for.
-
l2 – defaults to
0Amount 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)
- kwargs
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.