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# skfolio.prior.LoadingMatrixRegression

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### *class* skfolio.prior.LoadingMatrixRegression(linear_regressor=None, n_jobs=None)

Loading Matrix Regression estimator.

Estimate the loading matrix by fitting one linear regressor per asset.

* **Parameters:**
  **linear_regressor** *BaseEstimator, optional*
  : Linear regressor used to fit the factors on each asset separately.
    The default (`None`) is to use `LassoCV(fit_intercept=False)`.

  **n_jobs** *int, optional*
  : The number of jobs to run in parallel.
    <br/>
    When individual estimators are fast to train or predict,
    using `n_jobs > 1` can result in slower performance due
    to the parallelism overhead.
    <br/>
    The value `-1` means using all processors.
    The default (`None`) means 1 unless in a `joblib.parallel_backend` context.
* **Attributes:**
  **loading_matrix_** *ndarray of shape (n_assets, n_factors)*
  : The asset-by-factor loading (exposure) matrix.

  **intercepts_: ndarray of shape (n_assets,)**
  : The intercepts.

  **multi_output_regressor_: MultiOutputRegressor**
  : Fitted `sklearn.multioutput.MultiOutputRegressor`

### Methods

| [`fit`](#skfolio.prior.LoadingMatrixRegression.fit)(X, y, \*\*fit_params)   | Fit the Loading Matrix Regression Estimator.   |
|------------------------------------------------------------------------------|------------------------------------------------|
| [`get_metadata_routing`](#skfolio.prior.LoadingMatrixRegression.get_metadata_routing)()      | Get metadata routing of this object.           |
| [`get_params`](#skfolio.prior.LoadingMatrixRegression.get_params)([deep])          | Get parameters for this estimator.             |
| [`set_params`](#skfolio.prior.LoadingMatrixRegression.set_params)(\*\*params)      | Set the parameters of this estimator.          |

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#### fit(X, y, \*\*fit_params)

Fit the Loading Matrix Regression Estimator.

* **Parameters:**
  **X** *array-like of shape (n_observations, n_assets)*
  : Price returns of the assets.

  **y** *array-like of shape (n_observations, n_factors)*
  : Price returns of the factors.

  **\*\*fit_params** *dict*
  : Parameters to pass to the underlying estimators.
    Only available if `enable_metadata_routing=True`, which can be
    set by using `sklearn.set_config(enable_metadata_routing=True)`.
    See [Metadata Routing User Guide](https://skfolio.org/user_guide/metadata_routing.html.md#metadata-routing) for
    more details.
* **Returns:**
  **self** *LoadingMatrixRegression*
  : Fitted estimator.

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#### get_metadata_routing()

Get metadata routing of this object.

Please check [User Guide](https://skfolio.org/user_guide/metadata_routing.html.md#metadata-routing) on how the routing
mechanism works.

* **Returns:**
  **routing** *MetadataRequest*
  : A `MetadataRequest` encapsulating
    routing information.

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#### 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.

<a id="skfolio.prior.LoadingMatrixRegression.set_params"></a>

#### 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.