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Machine Learning & Data Science · head to head

JMP vs scikit-learn

JMP logo

JMP

Machine Learning & Data Science

Statistical discovery software from SAS

From
Free
Rated
-
Full reviewWebsite
S

scikit-learn

Machine Learning & Data Science

Machine learning in Python

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: JMP the Internet Archive's capture of JMP's homepage on 13 January 2020 named five distinct editions, JMP, JMP Live, JMP Pro, JMP Clinical, and JMP Genomics, each targeting a different analysis use case, with no price figure published for any.; scikit-learn no GPU acceleration by default; limited optional GPU support requires external arrays
  • They diverge on capability: JMP covers Interactive statistics, scikit-learn covers Classification algorithms.

Where they differ

Only the attributes on which JMP and scikit-learn actually diverge.

Attributes where JMP and scikit-learn differ
AttributeJMPscikit-learn
Pricing modelsubscriptionUnknown
PlatformsMac, WindowsPython, Linux, macOS, Windows
Founded19762007

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 JMP

  • Interactive statistics
  • Dynamic visualization
  • Design of experiments
  • Predictive modeling
  • Quality control
  • SAS
  • Python
  • R

Only in scikit-learn

  • Classification algorithms
  • Regression models
  • Clustering methods
  • Dimensionality reduction
  • Model selection
  • NumPy
  • SciPy
  • Pandas

Both cover

  • Mac support
  • Windows support

What people use each for

The jobs each tool is most often brought in to do.

JMP

  • Machine learning
  • Data analysis
  • Model training
  • Predictive analytics

scikit-learn

  • Machine learning
  • Data analysis
  • Model training
  • Predictive analytics

Both are used for machine learning, data analysis, model training, predictive analytics, on those jobs the choice comes down to price and fit rather than capability.

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

JMP

  • The Internet Archive's capture of JMP's homepage on 13 January 2020 named five distinct editions, JMP, JMP Live, JMP Pro, JMP Clinical, and JMP Genomics, each targeting a different analysis use case, with no price figure published for any.

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

Pricing, plan by plan

JMP

Free
  • TrialFree
    • 30-day trial
    • Full features
  • JMP$1785/year
    • Core JMP
    • Standard features

scikit-learn

Free

No published plan breakdown. See the scikit-learn review.

Which should you pick?

Choose JMP if

  • You need interactive statistics.
  • You want to start without paying.
  • You work on Mac, Windows.
  • You also want dynamic visualization.
JMP review

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.
scikit-learn review

Questions people ask

Is JMP or scikit-learn better?
Neither clearly leads. JMP starts at Free and scikit-learn at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, JMP or scikit-learn?
JMP starts at Free and scikit-learn at Free.
Does JMP or scikit-learn run on more platforms?
JMP runs on Mac, Windows. scikit-learn runs on Python, Linux, macOS, Windows.
Can I use JMP for free?
Both have a free tier, so you can try either at no cost before committing.
What is JMP best used for?
JMP is most often used for machine learning, data analysis, model training, predictive analytics.
What can JMP do that scikit-learn cannot?
JMP covers Interactive statistics, Dynamic visualization, Design of experiments, Predictive modeling. scikit-learn covers Classification algorithms, Regression models, Clustering methods, Dimensionality reduction. Both handle 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.

Source
scikit-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.

Source
scikit-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.

Source
scikit-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.

Source
scikit-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.

Source
scikit-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.

Source
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