Need advice about which tool to choose?Ask the StackShare community!

H2O

103
166
+ 1
4
scikit-learn

868
915
+ 1
35
Add tool

H2O vs scikit-learn: What are the differences?

Developers describe H2O as "H2O.ai AI for Business Transformation". H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark. On the other hand, scikit-learn is detailed as "Easy-to-use and general-purpose machine learning in Python". scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

H2O and scikit-learn can be categorized as "Machine Learning" tools.

H2O and scikit-learn are both open source tools. scikit-learn with 35.7K GitHub stars and 17.4K forks on GitHub appears to be more popular than H2O with 4.12K GitHub stars and 1.5K GitHub forks.

Repro, Home61, and MonkeyLearn are some of the popular companies that use scikit-learn, whereas H2O is used by Badgeville, BlueData, and Shaw Academy. scikit-learn has a broader approval, being mentioned in 70 company stacks & 39 developers stacks; compared to H2O, which is listed in 7 company stacks and 4 developer stacks.

Decisions about H2O and scikit-learn

A large part of our product is training and using a machine learning model. As such, we chose one of the best coding languages, Python, for machine learning. This coding language has many packages which help build and integrate ML models. For the main portion of the machine learning, we chose PyTorch as it is one of the highest quality ML packages for Python. PyTorch allows for extreme creativity with your models while not being too complex. Also, we chose to include scikit-learn as it contains many useful functions and models which can be quickly deployed. Scikit-learn is perfect for testing models, but it does not have as much flexibility as PyTorch. We also include NumPy and Pandas as these are wonderful Python packages for data manipulation. Also for testing models and depicting data, we have chosen to use Matplotlib and seaborn, a package which creates very good looking plots. Matplotlib is the standard for displaying data in Python and ML. Whereas, seaborn is a package built on top of Matplotlib which creates very visually pleasing plots.

See more
Get Advice from developers at your company using Private StackShare. Sign up for Private StackShare.
Learn More
Pros of H2O
Pros of scikit-learn
  • 1
    Highly customizable
  • 1
    Very fast and powerful
  • 1
    Auto ML is amazing
  • 1
    Super easy to use
  • 20
    Scientific computing
  • 15
    Easy

Sign up to add or upvote prosMake informed product decisions

Cons of H2O
Cons of scikit-learn
  • 1
    Not very popular
  • 1
    Limited

Sign up to add or upvote consMake informed product decisions

What is H2O?

H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.

What is scikit-learn?

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

Need advice about which tool to choose?Ask the StackShare community!

Jobs that mention H2O and scikit-learn as a desired skillset
What companies use H2O?
What companies use scikit-learn?
See which teams inside your own company are using H2O or scikit-learn.
Sign up for Private StackShareLearn More

Sign up to get full access to all the companiesMake informed product decisions

What tools integrate with H2O?
What tools integrate with scikit-learn?

Sign up to get full access to all the tool integrationsMake informed product decisions

Blog Posts

+42
46
39206
What are some alternatives to H2O and scikit-learn?
TensorFlow
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
DataRobot
It is an enterprise-grade predictive analysis software for business analysts, data scientists, executives, and IT professionals. It analyzes numerous innovative machine learning algorithms to establish, implement, and build bespoke predictive models for each situation.
PyTorch
PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
Keras
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
CUDA
A parallel computing platform and application programming interface model,it enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.
See all alternatives