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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.
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.
Pros of H2O
- Highly customizable2
- Very fast and powerful2
- Auto ML is amazing2
- Super easy to use2
Pros of scikit-learn
- Scientific computing23
- Easy18
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Cons of H2O
- Not very popular1
Cons of scikit-learn
- Limited2
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What is H2O?
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