How to build GLM models with Dask?
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How to build GLM models with Dask?

How to build GLM models with Dask?

This recipe helps you build GLM models with Dask

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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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