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
What Users are saying..
Savvy Sahai
Data Science Intern, Capgemini
As a student looking to break into the field of data engineering and data science, one can get really confused as to which path to take. Very few ways to do it are Google, YouTube, etc. I was one of... Read More
Relevant Projects
Machine Learning Projects
Data Science Projects
Python Projects for Data Science
Data Science Projects in R
Machine Learning Projects for Beginners
Deep Learning Projects
Neural Network Projects
Tensorflow Projects
NLP Projects
Kaggle Projects
IoT Projects
Big Data Projects
Hadoop Real-Time Projects Examples
Spark Projects
Data Analytics Projects for Students
You might also like
Data Science Tutorial
Data Scientist Salary
How to Become a Data Scientist
Data Analyst vs Data Scientist
Data Scientist Resume
Data Science Projects for Beginners
Machine Learning Engineer
Machine Learning Projects for Beginners
Datasets
Pandas Dataframe
Machine Learning Algorithms
Regression Analysis
MNIST Dataset
Data Science Interview Questions
Python Data Science Interview Questions
Spark Interview Questions
Hadoop Interview Questions
Data Analyst Interview Questions
Machine Learning Interview Questions
AWS vs Azure
Hadoop Architecture
Spark Architecture
Relevant Projects
Build Piecewise and Spline Regression Models in Python
In this Regression Project, you will learn how to build a piecewise and spline regression model from scratch in Python to predict the points scored by a sports team.
Learn to Build a Polynomial Regression Model from Scratch
In this Machine Learning Regression project, you will learn to build a polynomial regression model to predict points scored by the sports team.
Machine Learning Project to Forecast Rossmann Store Sales
In this machine learning project you will work on creating a robust prediction model of Rossmann's daily sales using store, promotion, and competitor data.
Personalized Medicine: Redefining Cancer Treatment
In this Personalized Medicine Machine Learning Project you will learn to classify genetic mutations on the basis of medical literature into 9 classes.
Build a Multi ClassText Classification Model using Naive Bayes
Implement the Naive Bayes Algorithm to build a multi class text classification model in Python.
Build a CNN Model with PyTorch for Image Classification
In this deep learning project, you will learn how to build an Image Classification Model using PyTorch CNN
OpenCV Project for Beginners to Learn Computer Vision Basics
In this OpenCV project, you will learn computer vision basics and the fundamentals of OpenCV library using Python.
Build a Review Classification Model using Gated Recurrent Unit
In this Machine Learning project, you will build a classification model in python to classify the reviews of an app on a scale of 1 to 5 using Gated Recurrent Unit.
Build an optimal End-to-End MLOps Pipeline and Deploy on GCP
Learn how to build and deploy an end-to-end optimal MLOps Pipeline for Loan Eligibility Prediction Model in Python on GCP
Multilabel Classification Project for Predicting Shipment Modes
Multilabel Classification Project to build a machine learning model that predicts the appropriate mode of transport for each shipment, using a transport dataset with 2000 unique products. The project explores and compares four different approaches to multilabel classification, including naive independent models, classifier chains, natively multilabel models, and multilabel to multiclass approaches.