dask_ml.linear_model.LogisticRegression

`dask_ml.linear_model`.LogisticRegression¶

class dask_ml.linear_model.LogisticRegression(penalty='l2', dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1.0, class_weight=None, random_state=None, solver='admm', max_iter=100, multi_class='ovr', verbose=0, warm_start=False, n_jobs=1, solver_kwargs=None)¶

Esimator for logistic regression.

Parameters

penaltystr or Regularizer, default ‘l2’

Regularizer to use. Only relevant for the ‘admm’, ‘lbfgs’ and ‘proximal_grad’ solvers.

For string values, only ‘l1’ or ‘l2’ are valid.

dualbool

Ignored

tolfloat, default 1e-4

The tolerance for convergence.

Cfloat

Regularization strength. Note that dask-glm solvers use the parameterization \(\lambda = 1 / C\)

fit_interceptbool, default True

Specifies if a constant (a.k.a. bias or intercept) should be added to the decision function.

intercept_scalingbool

Ignored

class_weightdict or ‘balanced’

Ignored

random_stateint, RandomState, or None

The seed of the pseudo random number generator to use when shuffling the data. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random. Used when solver == ‘sag’ or ‘liblinear’.

solver{‘admm’, ‘gradient_descent’, ‘newton’, ‘lbfgs’, ‘proximal_grad’}

Solver to use. See Algorithms for details

max_iterint, default 100

Maximum number of iterations taken for the solvers to converge.

multi_classstr, default ‘ovr’

Ignored. Multiclass solvers not currently supported.

verboseint, default 0

Ignored

warm_startbool, default False

Ignored

n_jobsint, default 1

Ignored

solver_kwargsdict, optional, default None

Extra keyword arguments to pass through to the solver.

Attributes

coef_array, shape (n_classes, n_features): The learned value for the model’s coefficients
intercept_float of None: The learned value for the intercept, if one was added to the model

Examples

>>> from dask_glm.datasets import make_classification
>>> X, y = make_classification()
>>> lr = LogisticRegression()
>>> lr.fit(X, y)
>>> lr.decision_function(X)
>>> lr.predict(X)
>>> lr.predict_proba(X)
>>> lr.score(X, y)

Methods

`decision_function`(X)	Predict confidence scores for samples in X.
`fit`(X[, y])	Fit the model on the training data
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`predict`(X)	Predict class labels for samples in X.
`predict_proba`(X)	Probability estimates for samples in X.
`score`(X, y)	The mean accuracy on the given data and labels
`set_params`(**params)	Set the parameters of this estimator.

__init__(penalty='l2', dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1.0, class_weight=None, random_state=None, solver='admm', max_iter=100, multi_class='ovr', verbose=0, warm_start=False, n_jobs=1, solver_kwargs=None)¶

dask_ml.linear_model.LinearRegression

dask_ml.linear_model.PoissonRegression

dask_ml.linear_model.LogisticRegression

dask_ml.linear_model.LogisticRegression¶

`dask_ml.linear_model`.LogisticRegression¶