# CD

### *class* cuml.dask.solvers.CD(, client=None, \*\*kwargs)[[source]](https://github.com/NVIDIA/cuml/blob/93418baf5/python/cuml/cuml/dask/solvers/cd.py#L16)

Multi-node Multi-GPU Coordinate Descent Solver.

This solver can be used for linear regression with L1 (Lasso) and/or L2
(Ridge) regularization.

* **Parameters:**
  **client**
  : Dask client to use

  **\*\*kwargs**
  : Additional arguments passed to the underlying single-GPU CD solver

### Methods

| [`fit`](#cuml.dask.solvers.CD.fit)(X, y)             | Fit the model with X and y.                           |
|------------------------------------------------------------------------|-------------------------------------------------------|
| [`predict`](#cuml.dask.solvers.CD.predict)(X[, delayed]) | Make predictions for X and returns a dask collection. |
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#### fit(X, y)[[source]](https://github.com/NVIDIA/cuml/blob/93418baf5/python/cuml/cuml/dask/solvers/cd.py#L36)

Fit the model with X and y.

* **Parameters:**
  **X**
  : Features for regression

  **y**
  : Labels (outcome values)

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#### get_combined_model()[[source]](https://github.com/NVIDIA/cuml/blob/93418baf5/python/cuml/cuml/dask/common/base.py#L57)

Return single-GPU model for serialization

* **Returns:**
  **model**
  : yet been trained.

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#### predict(X, delayed=True)[[source]](https://github.com/NVIDIA/cuml/blob/93418baf5/python/cuml/cuml/dask/solvers/cd.py#L54)

Make predictions for X and returns a dask collection.

* **Parameters:**
  **X**
  : Distributed dense matrix (floats or doubles) of shape
    (n_samples, n_features).

  **delayed**
  : Whether to do a lazy prediction (and return Delayed objects) or an
    eagerly executed one.
* **Returns:**
  **y**

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