Machine Learning & Data Science · head to head
scikit-learn vs Weka
scikit-learn
Machine Learning & Data Science
Machine learning in Python
- From
- Free
- Rated
- -
Weka
Machine Learning & Data Science
Collection of machine learning algorithms
- From
- Free
- Rated
- -
The short version
- Each has a real cost: scikit-learn no GPU acceleration by default; limited optional GPU support requires external arrays; Weka the package management system needs an internet connection to download and install packages, so an air-gapped install gets only the core distribution
- They diverge on capability: scikit-learn covers Classification algorithms, Weka covers Classification.
Where they differ
Only the attributes on which scikit-learn and Weka actually diverge.
| Attribute | scikit-learn | Weka |
|---|---|---|
| Pricing model | Unknown | open-source |
| Platforms | Python, Linux, macOS, Windows | Linux, Mac, Windows |
| Founded | 2007 | 1993 |
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Machine Learning & Data Science).
What each one covers
Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.
Only in scikit-learn
- Classification algorithms
- Regression models
- Clustering methods
- Dimensionality reduction
- Model selection
- NumPy
- SciPy
- Pandas
Only in Weka
- Classification
- Regression
- Clustering
- Association rules
- Feature selection
- Java
- R
- Python
Both cover
- Linux support
- Mac support
- Windows support
What people use each for
The jobs each tool is most often brought in to do.
scikit-learn
- Machine learningnot Weka
- Data analysisnot Weka
- Model trainingnot Weka
- Predictive analyticsnot Weka
Weka
- Teaching and exploring classic machine learning algorithms through a GUInot scikit-learn
- Running data mining experiments and preprocessing without writing codenot scikit-learn
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
scikit-learn
- No GPU acceleration by default; limited optional GPU support requires external arrays
- Single-machine only; no built-in distributed computing across clusters
- All datasets must fit entirely in RAM; no out-of-core learning
- No production-grade deep learning; neural network support limited to basic multilayer perceptron
- No reinforcement learning algorithms
Weka
- The package management system needs an internet connection to download and install packages, so an air-gapped install gets only the core distribution
- Weka is split into a stable 3.8 branch that receives only bug fixes and compatibility-safe upgrades and a 3.9 development branch that may receive features that break compatibility
- Weka requires a 64-bit Java VM; the bundled installers ship Bellsoft OpenJDK 25 per platform and architecture
Pricing, plan by plan
scikit-learn
FreeNo published plan breakdown. See the scikit-learn review.
Weka
Free- Open SourceFree
- All ML algorithms
- GUI and CLI
- Java API
Which should you pick?
Choose scikit-learn if
- You need classification algorithms.
- You want to start without paying.
- You work on Python, Linux, macOS, Windows.
- You also want regression models.
Choose Weka if
- You need classification.
- You want to start without paying.
- You work on Linux, Mac, Windows.
- You also want regression.
Questions people ask
- Is scikit-learn or Weka better?
- Neither clearly leads. scikit-learn starts at Free and Weka at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, scikit-learn or Weka?
- scikit-learn starts at Free and Weka at Free.
- Does scikit-learn or Weka run on more platforms?
- scikit-learn runs on Python, Linux, macOS, Windows. Weka runs on Linux, Mac, Windows.
- Can I use scikit-learn for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is scikit-learn best used for?
- scikit-learn is most often used for machine learning, data analysis, model training, predictive analytics. Of those, machine learning and data analysis are not what Weka is typically brought in for.
- What can scikit-learn do that Weka cannot?
- scikit-learn covers Classification algorithms, Regression models, Clustering methods, Dimensionality reduction. Weka covers Classification, Regression, Clustering, Association rules. Both handle Linux support, Mac support, Windows support.
Answered from the vendors’ own pages
scikit-learn: Does scikit-learn support GPU acceleration?
Scikit-learn has no native GPU support by design to keep installation simple and cross-platform. Since 2023, a limited number of estimators can run on GPUs if input data is provided as PyTorch or CuPy arrays, but this requires additional setup.
Sourcescikit-learn: Can scikit-learn handle datasets larger than RAM?
No. Scikit-learn is built on NumPy which requires all data to fit in memory, and NumPy operates on single-machine CPUs only. For very large datasets, consider Spark MLlib or distributed alternatives.
Sourcescikit-learn: Is scikit-learn free to use commercially?
Yes. Scikit-learn is open source under the BSD license, which allows free commercial use, modification, and distribution.
Sourcescikit-learn: What neural network capabilities does scikit-learn have?
Scikit-learn includes only a basic multilayer perceptron (MLPClassifier and MLPRegressor) for simple feedforward networks. For serious deep learning, use PyTorch, TensorFlow, or Keras instead.
Sourcescikit-learn: Does scikit-learn include natural language processing?
Scikit-learn has minimal NLP support limited to basic text feature extraction and vectorization. For comprehensive text processing, use spaCy or NLTK instead.
Sourcescikit-learn: When was scikit-learn first released?
Scikit-learn's first public release was February 1, 2010, following its start as a Google Summer of Code project in 2007.
SourceRelated pages
More on scikit-learn
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