Implementation:Rapidsai Cuml MBSGDRegressor

Knowledge Sources	Rapidsai_Cuml
Domains	Machine_Learning, Regression
Last Updated	2026-02-08 12:00 GMT

Overview

MBSGDRegressor provides a GPU-accelerated linear regression model fitted by minimizing a regularized empirical loss using mini-batch stochastic gradient descent.

Description

The MBSGDRegressor class implements linear regression using mini-batch stochastic gradient descent (SGD). It currently only supports the 'squared_loss' loss function for standard linear regression. Regularization options include L1, L2, Elastic-Net, or no penalty, controlled by the penalty and alpha parameters.

This implementation is experimental and uses a different algorithm than scikit-learn's SGDRegressor. The model processes data in configurable batch sizes and supports multiple learning rate schedules: constant, inverse scaling, and adaptive. The fit method internally delegates to cuML's fit_sgd solver. The class inherits prediction logic from LinearPredictMixin.

To improve results, the documentation recommends reducing batch size, increasing eta0, and increasing the number of epochs, since analyzing data in small batches with a low learning rate may limit how much the model can learn per pass.

Usage

Use MBSGDRegressor for regression tasks where you want GPU-accelerated mini-batch SGD training with configurable regularization. It is suitable for large datasets where full-batch methods are too slow. Note that it is experimental and may require tuning of learning rate and batch size parameters to achieve good results.

Code Reference

Source Location

Repository: Rapidsai_Cuml
File: python/cuml/cuml/linear_model/mbsgd_regressor.py

Signature

class MBSGDRegressor(Base, LinearPredictMixin, RegressorMixin, FMajorInputTagMixin):
    def __init__(
        self,
        *,
        loss="squared_loss",
        penalty="l2",
        alpha=0.0001,
        l1_ratio=0.15,
        fit_intercept=True,
        epochs=1000,
        tol=1e-3,
        shuffle=True,
        learning_rate="constant",
        eta0=0.001,
        power_t=0.5,
        batch_size=32,
        n_iter_no_change=5,
        verbose=False,
        output_type=None,
    )

Import

from cuml.linear_model import MBSGDRegressor

I/O Contract

Inputs

Name	Type	Required	Description
loss	str	No	Loss function. Only 'squared_loss' is supported. Default is 'squared_loss'.
penalty	str or None	No	Regularization: 'l1', 'l2', 'elasticnet', or None. Default is 'l2'.
alpha	float	No	Regularization strength constant. Default is 0.0001.
l1_ratio	float	No	Elastic-Net mixing parameter (0 <= l1_ratio <= 1). Only used when penalty='elasticnet'. Default is 0.15.
fit_intercept	bool	No	Whether to fit a bias term. Default is True.
epochs	int	No	Number of passes over the entire dataset. Default is 1000.
tol	float	No	Stopping tolerance: stops if current_loss > previous_loss - tol. Default is 1e-3.
shuffle	bool	No	Whether to shuffle training data after each epoch. Default is True.
learning_rate	str	No	Learning rate schedule: 'constant', 'invscaling', or 'adaptive'. Default is 'constant'.
eta0	float	No	Initial learning rate. Default is 0.001.
power_t	float	No	Exponent for invscaling learning rate. Default is 0.5.
batch_size	int	No	Number of samples per mini-batch. Default is 32.
n_iter_no_change	int	No	Number of epochs without improvement before stopping or adapting. Default is 5.
verbose	int or bool	No	Sets logging level. Default is False.
output_type	str or None	No	Return results in the indicated output type.

Outputs

Name	Type	Description
coef_	array (n_features,)	The learned model coefficients.
intercept_	float	Independent term (bias). Zero if fit_intercept is False.

Usage Examples

Basic Usage

import cupy as cp
import cuml

# Create sample data
X = cp.array([[1, 1], [1, 2], [2, 2], [2, 3]], dtype=cp.float32)
y = cp.array([1, 1, 2, 2], dtype=cp.float32)

# Fit MBSGDRegressor
model = cuml.MBSGDRegressor(
    loss="squared_loss",
    penalty="l2",
    alpha=0.0001,
    epochs=1000,
    eta0=0.001,
    batch_size=32
).fit(X, y)

# Predict on new data
X_test = cp.asarray([[3, 5], [2, 5]], dtype=cp.float32)
predictions = model.predict(X_test)
print(predictions)

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