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 afloat
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.