CD#
- class cuml.dask.solvers.CD(*, client=None, **kwargs)[source]#
Multi-node Multi-GPU Coordinate Descent Solver.
This solver can be used for linear regression with L1 (Lasso) and/or L2 (Ridge) regularization.
- Parameters:
- clientdask.distributed.Client, optional
Dask client to use
- **kwargsdict
Additional arguments passed to the underlying single-GPU CD solver
Methods
fit(X, y)Fit the model with X and y.
predict(X[, delayed])Make predictions for X and returns a dask collection.
- fit(X, y)[source]#
Fit the model with X and y.
- Parameters:
- XDask cuDF DataFrame or CuPy backed Dask Array (n_rows, n_features)
Features for regression
- yDask cuDF DataFrame or CuPy backed Dask Array (n_rows, 1)
Labels (outcome values)
- predict(X, delayed=True)[source]#
Make predictions for X and returns a dask collection.
- Parameters:
- XDask cuDF DataFrame or CuPy backed Dask Array (n_rows, n_features)
Distributed dense matrix (floats or doubles) of shape (n_samples, n_features).
- delayedbool (default = True)
Whether to do a lazy prediction (and return Delayed objects) or an eagerly executed one.
- Returns:
- yDask cuDF DataFrame or CuPy backed Dask Array (n_rows, 1)