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# `sklearn.linear_model`.SGDRegressor

class sklearn.linear_model.SGDRegressor(loss='squared_error', *, penalty='l2', alpha=0.0001, l1_ratio=0.15, fit_intercept=True, max_iter=1000, tol=0.001, shuffle=True, verbose=0, epsilon=0.1, random_state=None, learning_rate='invscaling', eta0=0.01, power_t=0.25, early_stopping=False, validation_fraction=0.1, n_iter_no_change=5, warm_start=False, average=False)

  Linear model fitted by minimizing a regularized empirical loss with SGD.

  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.

  Read more in the User Guide.

  Parameters:
    loss str, default=’squared_error’

      The loss function to be used. The possible values are ‘squared_error’, ‘huber’, ‘epsilon_insensitive’, or ‘squared_epsilon_insensitive’

      The ‘squared_error’ refers to the ordinary least squares fit. ‘huber’ modifies ‘squared_error’ to focus less on getting outliers correct by switching from squared to linear loss past a distance of epsilon. ‘epsilon_insensitive’ ignores errors less than epsilon and is linear past that; this is the loss function used in SVR. ‘squared_epsilon_insensitive’ is the same but becomes squared loss past a tolerance of epsilon.

      More details about the losses formulas can be found in the User Guide.

    penalty{‘l2’, ‘l1’, ‘elasticnet’, None}, default=’l2’

      The penalty (aka regularization term) to be used. Defaults to ‘l2’ which is the standard regularizer for linear SVM models. ‘l1’ and ‘elasticnet’ might bring sparsity to the model (feature selection) not achievable with ‘l2’. No penalty is added when set to `None`.

    alpha float, default=0.0001

      Constant that multiplies the regularization term. The higher the value, the stronger the regularization. Also used to compute the learning rate when set to `learning_rate` is set to ‘optimal’.

    l1_ratio float, default=0.15

      The Elastic Net mixing parameter, with 0 <= l1_ratio <= 1. l1_ratio=0 corresponds to L2 penalty, l1_ratio=1 to L1. Only used if `penalty` is ‘elasticnet’.

    fit_intercept bool, default=True

      Whether the intercept should be estimated or not. If False, the data is assumed to be already centered.

    max_iter int, default=1000

      The maximum number of passes over the training data (aka epochs). It only impacts the behavior in the `fit` method, and not the `partial_fit` method.

      New in version 0.19.

    tol float or None, default=1e-3

      The stopping criterion. If it is not None, training will stop when (loss > best_loss - tol) for `n_iter_no_change` consecutive epochs. Convergence is checked against the training loss or the validation loss depending on the `early_stopping` parameter.

      New in version 0.19.

    shuffle bool, default=True

      Whether or not the training data should be shuffled after each epoch.

    verbose int, default=0

      The verbosity level.

    epsilon float, default=0.1

      Epsilon in the epsilon-insensitive loss functions; only if `loss` is ‘huber’, ‘epsilon_insensitive’, or ‘squared_epsilon_insensitive’. For ‘huber’, determines the threshold at which it becomes less important to get the prediction exactly right. For epsilon-insensitive, any differences between the current prediction and the correct label are ignored if they are less than this threshold.

    random_state int, RandomState instance, default=None

      Used for shuffling the data, when `shuffle` is set to `True`. Pass an int for reproducible output across multiple function calls. See Glossary.

    learning_rate str, default=’invscaling’

      The learning rate schedule:

      - ‘constant’: `eta = eta0`

      - ‘optimal’: `eta = 1.0 / (alpha * (t + t0))` where t0 is chosen by a heuristic proposed by Leon Bottou.

      - ‘invscaling’: `eta = eta0 / pow(t, power_t)`

      - ‘adaptive’: eta = eta0, as long as the training keeps decreasing. Each time n_iter_no_change consecutive epochs fail to decrease the training loss by tol or fail to increase validation score by tol if early_stopping is True, the current learning rate is divided by 5.

        > New in version 0.20: Added ‘adaptive’ option

    eta0 float, default=0.01

      The initial learning rate for the ‘constant’, ‘invscaling’ or ‘adaptive’ schedules. The default value is 0.01.

    power_t float, default=0.25

      The exponent for inverse scaling learning rate.

    early_stopping bool, default=False

      Whether to use early stopping to terminate training when validation score is not improving. If set to True, it will automatically set aside a fraction of training data as validation and terminate training when validation score returned by the `score` method is not improving by at least `tol` for `n_iter_no_change` consecutive epochs.

      New in version 0.20: Added ‘early_stopping’ option

    validation_fraction float, default=0.1

      The proportion of training data to set aside as validation set for early stopping. Must be between 0 and 1. Only used if `early_stopping` is True.

      New in version 0.20: Added ‘validation_fraction’ option

    n_iter_no_change int, default=5

      Number of iterations with no improvement to wait before stopping fitting. Convergence is checked against the training loss or the validation loss depending on the `early_stopping` parameter.

      New in version 0.20: Added ‘n_iter_no_change’ option

    warm_start bool, default=False

      When set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution. See the Glossary.

      Repeatedly calling fit or partial_fit when warm_start is True can result in a different solution than when calling fit a single time because of the way the data is shuffled. If a dynamic learning rate is used, the learning rate is adapted depending on the number of samples already seen. Calling `fit` resets this counter, while `partial_fit` will result in increasing the existing counter.

    average bool or int, default=False

      When set to True, computes the averaged SGD weights across all updates and stores the result in the `coef_` attribute. If set to an int greater than 1, averaging will begin once the total number of samples seen reaches `average`. So `average=10` will begin averaging after seeing 10 samples.

  Attributes:
    coef_ndarray of shape (n_features,)

      Weights assigned to the features.

    intercept_ndarray of shape (1,)

      The intercept term.

    n_iter_int

      The actual number of iterations before reaching the stopping criterion.

    t_int

      Number of weight updates performed during training. Same as `(n_iter_ * n_samples + 1)`.

    n_features_in_int

      Number of features seen during fit.

      New in version 0.24.

    feature_names_in_ndarray of shape (`n_features_in_`,)

      Names of features seen during fit. Defined only when `X` has feature names that are all strings.

      New in version 1.0.

  See also

  `HuberRegressor`

    Linear regression model that is robust to outliers.

  `Lars`

    Least Angle Regression model.

  `Lasso`

    Linear Model trained with L1 prior as regularizer.

  `RANSACRegressor`

    RANSAC (RANdom SAmple Consensus) algorithm.

  `Ridge`

    Linear least squares with l2 regularization.

  `sklearn.svm.SVR`

    Epsilon-Support Vector Regression.

  `TheilSenRegressor`

    Theil-Sen Estimator robust multivariate regression model.

  Examples

      >>> import numpy as np
      >>> from sklearn.linear_model import SGDRegressor
      >>> from sklearn.pipeline import make_pipeline
      >>> from sklearn.preprocessing import StandardScaler
      >>> n_samples, n_features = 10, 5
      >>> rng = np.random.RandomState(0)
      >>> y = rng.randn(n_samples)
      >>> X = rng.randn(n_samples, n_features)
      >>> # Always scale the input. The most convenient way is to use a pipeline.
      >>> reg = make_pipeline(StandardScaler(),
      ...                     SGDRegressor(max_iter=1000, tol=1e-3))
      >>> reg.fit(X, y)
      Pipeline(steps=[('standardscaler', StandardScaler()),
                      ('sgdregressor', SGDRegressor())])

  Methods

  `densify`() | Convert coefficient matrix to dense array format.
  `fit`(X, y[, coef_init, intercept_init, ...]) | Fit linear model with Stochastic Gradient Descent.
  `get_metadata_routing`() | Get metadata routing of this object.
  `get_params`([deep]) | Get parameters for this estimator.
  `partial_fit`(X, y[, sample_weight]) | Perform one epoch of stochastic gradient descent on given samples.
  `predict`(X) | Predict using the linear model.
  `score`(X, y[, sample_weight]) | Return the coefficient of determination of the prediction.
  `set_fit_request`(*[, coef_init, ...]) | Request metadata passed to the `fit` method.
  `set_params`(**params) | Set the parameters of this estimator.
  `set_partial_fit_request`(*[, sample_weight]) | Request metadata passed to the `partial_fit` method.
  `set_score_request`(*[, sample_weight]) | Request metadata passed to the `score` method.
  `sparsify`() | Convert coefficient matrix to sparse format.

  densify()

    Convert coefficient matrix to dense array format.

    Converts the `coef_` member (back) to a numpy.ndarray. This is the default format of `coef_` and is required for fitting, so calling this method is only required on models that have previously been sparsified; otherwise, it is a no-op.

    Returns:
      self

        Fitted estimator.

  fit(X, y, coef_init=None, intercept_init=None, sample_weight=None)

    Fit linear model with Stochastic Gradient Descent.

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

        Training data.

      y ndarray of shape (n_samples,)

        Target values.

      coef_init ndarray of shape (n_features,), default=None

        The initial coefficients to warm-start the optimization.

      intercept_init ndarray of shape (1,), default=None

        The initial intercept to warm-start the optimization.

      sample_weight array-like, shape (n_samples,), default=None

        Weights applied to individual samples (1. for unweighted).

    Returns:
      self object

        Fitted `SGDRegressor` estimator.

  get_metadata_routing()

    Get metadata routing of this object.

    Please check User Guide on how the routing mechanism works.

    Returns:
      routing MetadataRequest

        A `MetadataRequest` encapsulating routing information.

  get_params(deep=True)

    Get parameters for this estimator.

    Parameters:
      deep bool, default=True

        If True, will return the parameters for this estimator and contained subobjects that are estimators.

    Returns:
      params dict

        Parameter names mapped to their values.

  partial_fit(X, y, sample_weight=None)

    Perform one epoch of stochastic gradient descent on given samples.

    Internally, this method uses `max_iter = 1`. Therefore, it is not guaranteed that a minimum of the cost function is reached after calling it once. Matters such as objective convergence and early stopping should be handled by the user.

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

        Subset of training data.

      y numpy array of shape (n_samples,)

        Subset of target values.

      sample_weight array-like, shape (n_samples,), default=None

        Weights applied to individual samples. If not provided, uniform weights are assumed.

    Returns:
      self object

        Returns an instance of self.

  predict(X)

    Predict using the linear model.

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

        Input data.

    Returns:
      ndarray of shape (n_samples,)

        Predicted target values per element in X.

  score(X, y, sample_weight=None)

    Return the coefficient of determination of the prediction.

    The coefficient of determination \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares `((y_true - y_pred)** 2).sum()` and \(v\) is the total sum of squares `((y_true - y_true.mean()) ** 2).sum()`. The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of `y`, disregarding the input features, would get a \(R^2\) score of 0.0.

    Parameters:
      X array-like of shape (n_samples, n_features)

        Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape `(n_samples, n_samples_fitted)`, where `n_samples_fitted` is the number of samples used in the fitting for the estimator.

      y array-like of shape (n_samples,) or (n_samples, n_outputs)

        True values for `X`.

      sample_weight array-like of shape (n_samples,), default=None

        Sample weights.

    Returns:
      score float

        \(R^2\) of `self.predict(X)` w.r.t. `y`.

    Notes

    The \(R^2\) score used when calling `score` on a regressor uses `multioutput='uniform_average'` from version 0.23 to keep consistent with default value of `r2_score`. This influences the `score` method of all the multioutput regressors (except for `MultiOutputRegressor`).

  set_fit_request(*, coef_init: bool | None | str = '$UNCHANGED$', intercept_init: bool | None | str = '$UNCHANGED$', sample_weight: bool | None | str = '$UNCHANGED$') → SGDRegressor

    Request metadata passed to the `fit` method.

    Note that this method is only relevant if `enable_metadata_routing=True` (see `sklearn.set_config`). Please see User Guide on how the routing mechanism works.

    The options for each parameter are:

    - `True`: metadata is requested, and passed to `fit` if provided. The request is ignored if metadata is not provided.

    - `False`: metadata is not requested and the meta-estimator will not pass it to `fit`.

    - `None`: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

    - `str`: metadata should be passed to the meta-estimator with this given alias instead of the original name.

    The default (`sklearn.utils.metadata_routing.UNCHANGED`) retains the existing request. This allows you to change the request for some parameters and not others.

    New in version 1.3.

    Note

    This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a `Pipeline`. Otherwise it has no effect.

    Parameters:
      coef_init str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `coef_init` parameter in `fit`.

      intercept_init str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `intercept_init` parameter in `fit`.

      sample_weight str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `sample_weight` parameter in `fit`.

    Returns:
      self object

        The updated object.

  set_params(**params)

    Set the parameters of this estimator.

    The method works on simple estimators as well as on nested objects (such as `Pipeline`). The latter have parameters of the form `<component>__<parameter>` so that it’s possible to update each component of a nested object.

    Parameters:
      **params dict

        Estimator parameters.

    Returns:
      self estimator instance

        Estimator instance.

  set_partial_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → SGDRegressor

    Request metadata passed to the `partial_fit` method.

    Note that this method is only relevant if `enable_metadata_routing=True` (see `sklearn.set_config`). Please see User Guide on how the routing mechanism works.

    The options for each parameter are:

    - `True`: metadata is requested, and passed to `partial_fit` if provided. The request is ignored if metadata is not provided.

    - `False`: metadata is not requested and the meta-estimator will not pass it to `partial_fit`.

    - `None`: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

    - `str`: metadata should be passed to the meta-estimator with this given alias instead of the original name.

    The default (`sklearn.utils.metadata_routing.UNCHANGED`) retains the existing request. This allows you to change the request for some parameters and not others.

    New in version 1.3.

    Note

    This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a `Pipeline`. Otherwise it has no effect.

    Parameters:
      sample_weight str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `sample_weight` parameter in `partial_fit`.

    Returns:
      self object

        The updated object.

  set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → SGDRegressor

    Request metadata passed to the `score` method.

    Note that this method is only relevant if `enable_metadata_routing=True` (see `sklearn.set_config`). Please see User Guide on how the routing mechanism works.

    The options for each parameter are:

    - `True`: metadata is requested, and passed to `score` if provided. The request is ignored if metadata is not provided.

    - `False`: metadata is not requested and the meta-estimator will not pass it to `score`.

    - `None`: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

    - `str`: metadata should be passed to the meta-estimator with this given alias instead of the original name.

    The default (`sklearn.utils.metadata_routing.UNCHANGED`) retains the existing request. This allows you to change the request for some parameters and not others.

    New in version 1.3.

    Note

    This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a `Pipeline`. Otherwise it has no effect.

    Parameters:
      sample_weight str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `sample_weight` parameter in `score`.

    Returns:
      self object

        The updated object.

  sparsify()

    Convert coefficient matrix to sparse format.

    Converts the `coef_` member to a scipy.sparse matrix, which for L1-regularized models can be much more memory- and storage-efficient than the usual numpy.ndarray representation.

    The `intercept_` member is not converted.

    Returns:
      self

        Fitted estimator.

    Notes

    For non-sparse models, i.e. when there are not many zeros in `coef_`, this may actually increase memory usage, so use this method with care. A rule of thumb is that the number of zero elements, which can be computed with `(coef_ == 0).sum()`, must be more than 50% for this to provide significant benefits.

    After calling this method, further fitting with the partial_fit method (if any) will not work until you call densify.

## Examples using `sklearn.linear_model.SGDRegressor`

Prediction Latency

Prediction Latency

SGD: Penalties

SGD: Penalties