How to build GLM models with Dask?

How to build GLM models with Dask?

How to build GLM models with Dask?

This recipe helps you build GLM models with Dask


Recipe Objective

How to build a GLM models with dask.

GLM models stands for Generalized Linear Models. It is mainly used to solve the regression problems containing continuous values.

The Dask-GLM project is nicely modulated, It allows different GLM families and Regularizers as well, It includes a relatively direct interface for implementing custom GLMs.

#! pip install dask_glm

Step 1- Importing Libraries

from dask_glm.datasets import make_regression import dask_glm.algorithms import dask

Step 2- Creating Regression model.

We will create the regression model and pass it through the persist to create the dataframe so that we get the partitions of 100 dask DataFrames.

x, y = make_regression(n_samples=2000, n_features=100, n_informative=5, chunksize=100) x, y = dask.persist(x, y) print(x) print(y)

Step 3- Applying the algorithm function.

algo = dask_glm.algorithms.admm(X, y, max_iter=5) algo

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