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MLlib VS imbalanced-learn

Compare MLlib VS imbalanced-learn and see what are their differences

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LibHunt tracks mentions of software libraries on relevant social networks. Based on that data, you can find the most popular projects and their alternatives. featured
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MLlib logo MLlib

MLlib is Spark's machine learning (ML) library that make practical machine learning scalable & provides ML Algorithms.

imbalanced-learn logo imbalanced-learn

Data Science and Machine Learning
  • MLlib Landing page
    Landing page //
    2023-06-12
  • imbalanced-learn Landing page
    Landing page //
    2023-07-14

MLlib videos

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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 ]

imbalanced-learn videos

No imbalanced-learn videos yet. You could help us improve this page by suggesting one.

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Category Popularity

0-100% (relative to MLlib and imbalanced-learn)

MLlib

imbalanced-learn

Data Science And Machine Learning
85 85%
Data Science And Machine Learning
15% 15
Python Tools
84 84%
Python Tools
16% 16
Data Science Tools
84 84%
Data Science Tools
16% 16
Software Libraries
50 50%
Software Libraries
50% 50

User comments

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Social recommendations and mentions

Based on our record, MLlib seems to be more popular. 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.

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: about 2 years ago

imbalanced-learn mentions (0)

We have not tracked any mentions of imbalanced-learn yet. Tracking of imbalanced-learn recommendations started around Mar 2021.

What are some alternatives?

When comparing MLlib and imbalanced-learn, 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.

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

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

Dataiku - Dataiku is the developer of DSS, the integrated development platform for data professionals to turn raw data into predictions.

OpenCV - OpenCV is the world's biggest computer vision library

DimML - The DimML programming language enables users to run any data solution on any website with only a single line of code.

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