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XGBoost VS MLlib

Compare XGBoost VS MLlib and see what are their differences

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Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

XGBoost logo XGBoost

XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable.

MLlib logo MLlib

MLlib is Spark's machine learning (ML) library that make practical machine learning scalable & provides ML Algorithms.
  • XGBoost Landing page
    Landing page //
    2023-07-30
  • MLlib Landing page
    Landing page //
    2023-06-12

XGBoost videos

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XGBoost Part 3: Mathematical Details

More videos:

  • Review - XGBoost A Scalable Tree Boosting System June 02, 2016
  • Review - Free Udemy Course - CatBoost vs XGBoost - Classification and Regression Modeling with Python

MLlib videos

+ Add

Using Spark Mllib Models in a Production Training and Serving Platform Experiences and ExtensionsA

More videos:

  • Review - Spark MLlib
  • Review - Announcement: LIVE on 26th July [ Spark SQL & MLLib ]

Category Popularity

0-100% (relative to XGBoost and MLlib)

XGBoost

MLlib

Data Science And Machine Learning
28 28%
Data Science And Machine Learning
72% 72
Business & Commerce
100 100%
Business & Commerce
0% 0
Data Science Tools
15 15%
Data Science Tools
85% 85
Online Services
100 100%
Online Services
0% 0

User comments

Share your experience with using XGBoost and MLlib. For example, how are they different and which one is better?
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Social recommendations and mentions

Based on our record, MLlib should be more popular than XGBoost. It has been mentiond 2 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

XGBoost mentions (1)

  • CS Internship Questions
    By the way, most of the time XGBoost works just as well for projects, would not recommend applying deep learning to every single problem you come across, it's something Stanford CS really likes to showcase when it's well known (1) that sometimes "smaller"/less complex models can perform just as well or have their own interpretive advantages and (2) it is well known within ML and DS communities that deep learning... Source: almost 2 years ago

MLlib mentions (2)

  • Predicting Diabetes In Patients - Apache Spark Machine Learning - 4 Easy Steps To Do This!
    The MLlib library gives us a very wide range of available Machine Learning algorithms and additional tools for standardisation, tokenisation and many others (for more information visit the official website Apache Spark MLlib). (Apache Spark Machine Learning predicting diabetes in patients). Source: about 2 years ago
  • How to distribute ML tasks across CPU and GPU?
    Totally agree with the current responses, especially for the purposes of understanding exactly what's going on under the hood, but did want to just call out the fact that you can simply use a machine learning library that's implemented in a distributed way. Examples would be MLlib From Spark and h2o. H2O in particular will take care of pretty much everything for you in terms of initializing a cluster, and has a... Source: over 2 years ago

What are some alternatives?

When comparing XGBoost and MLlib, you can also consider the following products

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

Open Text Magellan - OpenText Magellan - the power of AI in a pre-wired platform that augments decision making and accelerates your business. Learn more.

Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.

Google Cloud TPU - Custom-built for machine learning workloads, Cloud TPUs accelerate training and inference at scale.

NumPy - NumPy is the fundamental package for scientific computing with Python

Kira - Gain visibility into contract repositories, accelerate and improve the accuracy of contract review, mitigate risk of errors, win new business, and improve the value you provide to your clients.

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