# skfolio.distance.DistanceCorrelation ### *class* skfolio.distance.DistanceCorrelation(threshold=0.5) Distance Correlation estimator. Distance Correlation was introduced by Szekely [[1]](#rc85b9d7bb4d0-1) to capture non-linear dependencies. * **Parameters:** **threshold** *float, default=0.5* : Distance correlation threshold. * **Attributes:** **codependence_** *ndarray of shape (n_assets, n_assets)* : Codependence matrix. **distance_** *ndarray of shape (n_assets, n_assets)* : Distance matrix. **n_features_in_** *int* : Number of assets seen during `fit`. **feature_names_in_** *ndarray of shape (`n_features_in_`,)* : Names of assets seen during `fit`. Defined only when `X` has assets names that are all strings. ### Methods | [`fit`](#skfolio.distance.DistanceCorrelation.fit)(X[, y]) | Fit the Distance Correlation estimator. | |-------------------------------------------------------------------------|-------------------------------------------| | [`get_metadata_routing`](#skfolio.distance.DistanceCorrelation.get_metadata_routing)() | Get metadata routing of this object. | | [`get_params`](#skfolio.distance.DistanceCorrelation.get_params)([deep]) | Get parameters for this estimator. | | [`set_params`](#skfolio.distance.DistanceCorrelation.set_params)(\*\*params) | Set the parameters of this estimator. | ### References * **[1]** “Measuring and testing independence by correlation of distances” Gábor J. Szekely , 2005 #### fit(X, y=None) Fit the Distance Correlation estimator. * **Parameters:** **X** *array-like of shape (n_observations, n_assets)* : Price returns of the assets. **y** *Ignored* : Not used, present for API consistency by convention. * **Returns:** **self** *DistanceCorrelation* : Fitted estimator. #### 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. #### 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. #### 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 `__` so that it’s possible to update each component of a nested object. * **Parameters:** **\*\*params** *dict* : Estimator parameters. * **Returns:** **self** *estimator instance* : Estimator instance.