Class PassiveAggressiveRegressorScikitsLearnNode

Passive Aggressive Regressor

This node has been automatically generated by wrapping the ``sklearn.linear_model.passive_aggressive.PassiveAggressiveRegressor`` class
from the ``sklearn`` library.  The wrapped instance can be accessed
through the ``scikits_alg`` attribute.

Read more in the :ref:`User Guide <passive_aggressive>`.

**Parameters**


C : float
    Maximum step size (regularization). Defaults to 1.0.

epsilon : float
    If the difference between the current prediction and the correct label
    is below this threshold, the model is not updated.

fit_intercept : bool
    Whether the intercept should be estimated or not. If False, the
    data is assumed to be already centered. Defaults to True.

n_iter : int, optional
    The number of passes over the training data (aka epochs).
    Defaults to 5.

shuffle : bool, default=True
    Whether or not the training data should be shuffled after each epoch.

random_state : int seed, RandomState instance, or None (default)
    The seed of the pseudo random number generator to use when
    shuffling the data.

verbose : integer, optional
    The verbosity level

loss : string, optional
    The loss function to be used:

    - epsilon_insensitive: equivalent to PA-I in the reference paper.
    - squared_epsilon_insensitive: equivalent to PA-II in the reference
    - paper.


warm_start : bool, optional
    When set to True, reuse the solution of the previous call to fit as
    initialization, otherwise, just erase the previous solution.

**Attributes**

``coef_`` : array, shape = [1, n_features] if n_classes == 2 else [n_classes,            n_features]
    Weights assigned to the features.

``intercept_`` : array, shape = [1] if n_classes == 2 else [n_classes]
    Constants in decision function.

See also


SGDRegressor

**References**

Online Passive-Aggressive Algorithms
<http://jmlr.csail.mit.edu/papers/volume7/crammer06a/crammer06a.pdf>
K. Crammer, O. Dekel, J. Keshat, S. Shalev-Shwartz, Y. Singer - JMLR (2006)

Overrides: object.__init__

Predict using the linear model

This node has been automatically generated by wrapping the sklearn.linear_model.passive_aggressive.PassiveAggressiveRegressor class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

X : {array-like, sparse matrix}, shape (n_samples, n_features)

Returns

array, shape (n_samples,)

Predicted target values per element in X.

Overrides: Node.execute

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Fit linear model with Passive Aggressive algorithm.

This node has been automatically generated by wrapping the sklearn.linear_model.passive_aggressive.PassiveAggressiveRegressor class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

X : {array-like, sparse matrix}, shape = [n_samples, n_features]

Training data

y : numpy array of shape [n_samples]

Target values

coef_init : array, shape = [n_features]

The initial coefficients to warm-start the optimization.

intercept_init : array, shape = [1]

The initial intercept to warm-start the optimization.

Returns

self : returns an instance of self.

Overrides: Node.stop_training