__init__(self,
input_dim=None,
output_dim=None,
dtype=None,
**kwargs)
(Constructor)
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Linear model fitted by minimizing a regularized empirical loss with SGD
This node has been automatically generated by wrapping the scikits.learn.linear_model.sparse.stochastic_gradient.SGDRegressor class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.
SGD stands for Stochastic Gradient Descent: the gradient of the loss is
estimated each sample at a time and the model is updated along the way with
a decreasing strength schedule (aka learning rate).
The regularizer is a penalty added to the loss function that shrinks model
parameters towards the zero vector using either the squared euclidean norm
L2 or the absolute norm L1 or a combination of both (Elastic Net). If the
parameter update crosses the 0.0 value because of the regularizer, the
update is truncated to 0.0 to allow for learning sparse models and
achieve online feature selection.
This implementation works with data represented as dense numpy arrays
of floating point values for the features.
Parameters
- loss : str, 'squared_loss' or 'huber'
- The loss function to be used. Defaults to 'squared_loss' which
refers to the ordinary least squares fit. 'huber' is an epsilon
insensitive loss function for robust regression.
- penalty : str, 'l2' or 'l1' or 'elasticnet'
- The penalty (aka regularization term) to be used. Defaults to 'l2'
which is the standard regularizer for linear SVM models. 'l1' and
'elasticnet' migh bring sparsity to the model (feature selection)
not achievable with 'l2'.
- alpha : float
- Constant that multiplies the regularization term. Defaults to 0.0001
- rho : float
- The Elastic Net mixing parameter, with 0 < rho <= 1.
Defaults to 0.85.
- 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
- The number of passes over the training data (aka epochs).
Defaults to 5.
- shuffle: bool
- Whether or not the training data should be shuffled after each epoch.
Defaults to False.
- seed: int, optional
- The seed of the pseudo random number generator to use when
shuffling the data.
- verbose: integer, optional
- The verbosity level
- p : float
- Epsilon in the epsilon insensitive huber loss function;
only if
loss=='huber'.
- learning_rate : string, optional
The learning rate:
- constant: eta = eta0
- optimal: eta = 1.0/(t+t0)
- invscaling: eta = eta0 / pow(t, power_t) [default]
- eta0 : double, optional
- The initial learning rate [default 0.01].
- power_t : double, optional
- The exponent for inverse scaling learning rate [default 0.25].
Attributes
coef_ : array, shape = [n_features]- Weights asigned to the features.
intercept_ : array, shape = [1]- The intercept term.
Examples
>>> import numpy as np
>>> from scikits.learn import linear_model
>>> n_samples, n_features = 10, 5
>>> np.random.seed(0)
>>> y = np.random.randn(n_samples)
>>> X = np.random.randn(n_samples, n_features)
>>> clf = linear_model.sparse.SGDRegressor()
>>> clf.fit(X, y)
SGDRegressor(loss='squared_loss', power_t=0.25, shuffle=False, verbose=0,
n_iter=5, learning_rate='invscaling', fit_intercept=True,
penalty='l2', p=0.1, seed=0, eta0=0.01, rho=1.0, alpha=0.0001)
See also
RidgeRegression, ElasticNet, Lasso, SVR
- Overrides:
object.__init__
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