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JMP VS Scikit-learn

Compare JMP VS Scikit-learn and see what are their differences

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Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

JMP logo JMP

JMP is a data representation tool that empowers the engineers, mathematicians and scientists to explore the any of data visually.

Scikit-learn logo Scikit-learn

scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
  • JMP Landing page
    Landing page //
    2023-04-12
  • Scikit-learn Landing page
    Landing page //
    2022-05-06

JMP features and specs

  • User-friendly Interface
    JMP offers a drag-and-drop interface that is intuitive and easy to navigate, making it accessible for both beginners and advanced users.
  • Comprehensive Data Visualization
    The software provides robust tools for data visualization, enabling users to create a wide variety of charts, graphs, and plots that can help in understanding complex data sets.
  • Advanced Statistical Analysis
    JMP includes a wide range of advanced statistical techniques, such as regression analysis, ANOVA, and multivariate methods, which are suitable for rigorous data analysis.
  • Integration with R and Python
    The software supports integration with R and Python, allowing users to leverage additional functionalities not available in JMP alone.
  • Interactive Data Exploration
    JMP enables interactive data exploration, allowing users to dynamically manipulate data sets and instantly see the results of their changes.
  • Quality Control Features
    The software includes numerous quality control tools, making it ideal for industries where maintaining high standards is critical.

Possible disadvantages of JMP

  • Cost
    JMP is a commercial software with a relatively high price point, which may be a barrier for small businesses or individual users.
  • Learning Curve
    Despite its user-friendly interface, JMP has a steep learning curve for those unfamiliar with statistical analysis and data visualization techniques.
  • Resource Intensive
    The software can be resource-intensive, requiring significant computational power and memory, especially when handling large datasets.
  • Limited Collaboration Features
    JMP lacks extensive features for real-time collaboration compared to some of the more modern data science platforms.
  • Package Ecosystem
    While JMP is powerful, its ecosystem of add-ons and packages is not as extensive as that of R or Python, which might limit its utility for some specialized tasks.
  • OS Compatibility
    JMP is primarily designed for Windows and MacOS. Users on other operating systems might face compatibility issues or may need to use workarounds.

Scikit-learn features and specs

  • Ease of Use
    Scikit-learn provides a high-level interface for common machine learning algorithms, making it easy for beginners and professionals to implement complex models with minimal coding.
  • Extensive Documentation and Community Support
    The library has comprehensive documentation and a large, active community. This makes it easy to find tutorials, examples, and solutions to common problems.
  • Integration with Other Libraries
    Scikit-learn integrates well with other scientific computing libraries such as NumPy, SciPy, and pandas, allowing for seamless data manipulation and analysis.
  • Variety of Algorithms
    It offers a wide array of machine learning algorithms for tasks such as classification, regression, clustering, and dimensionality reduction.
  • Performance
    Designed with performance in mind, many of the algorithms are optimized and some even support multicore processing.

Possible disadvantages of Scikit-learn

  • Limited Deep Learning Support
    Scikit-learn is primarily focused on traditional machine learning algorithms and does not offer support for deep learning models, unlike libraries like TensorFlow or PyTorch.
  • Not Ideal for Large-Scale Data
    While Scikit-learn performs well for moderate-sized datasets, it may not be the best choice for extremely large datasets or big data applications.
  • Lack of Online Learning Algorithms
    The library has limited support for online learning algorithms, which are useful for scenarios where data arrives in a stream and model needs to be updated incrementally.
  • Less Flexibility in Customization
    It can be less flexible compared to lower-level libraries when highly customized or specific implementations are needed.
  • Dependency Overhead
    Scikit-learn relies on several other Python libraries like NumPy and SciPy, which might require users to manage multiple dependencies.

JMP videos

+ Add

Review Of The UAD Marshall JMP 2203 Plug-in From Universal Audio

More videos:

  • Demo - Marshall JMP-1 - In Depth Demo by Leon Todd
  • Review - Marshall JMP 1 Watt Combo - Blues Harmonica Amp Review

Scikit-learn videos

+ Add

Learning Scikit-Learn (AI Adventures)

More videos:

  • Review - Python Machine Learning Review | Learn python for machine learning. Learn Scikit-learn.

Category Popularity

0-100% (relative to JMP and Scikit-learn)

JMP

Scikit-learn

Technical Computing
100 100%
Technical Computing
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Data Dashboard
40 40%
Data Dashboard
60% 60
Data Science Tools
0 0%
Data Science Tools
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare JMP and Scikit-learn

JMP Reviews

25 Best Statistical Analysis Software
JMP is a top-rated tool for data exploration and analysis, delivering dynamic data visualization and an extensive selection of statistical tools to empower users to make well-informed decisions based on their findings.
Source: www.scijournal.org
Top 10 Free Statistical Analysis Software 2023
5. JMP Scripting Language (JSL) scripting is supported for automation, customisation, and expanding functionality.
Source: fellowshipbard.com
9 Best Analysis Software for PC 2023
JMP is a software analysis software that can perform data manipulations and mining. It is a perfect alternative to MS Excel, which is famous for visualization. JMP is available on a free-trial and premium-based plan. The trial plan allows the user to interact with the software before deciding whether to purchase it.
Source: pdf.wps.com

Scikit-learn Reviews

15 data science tools to consider using in 2021
Scikit-learn is an open source machine learning library for Python that's built on the SciPy and NumPy scientific computing libraries, plus Matplotlib for plotting data. It supports both supervised and unsupervised machine learning and includes numerous algorithms and models, called estimators in scikit-learn parlance. Additionally, it provides functionality for model...
Source: searchbusinessanalytics.techtarget.com

Social recommendations and mentions

Based on our record, Scikit-learn seems to be more popular. It has been mentiond 31 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.

JMP mentions (0)

We have not tracked any mentions of JMP yet. Tracking of JMP recommendations started around Mar 2021.

Scikit-learn mentions (31)

  • Must-Know 2025 Developer’s Roadmap and Key Programming Trends
    Python’s Growth in Data Work and AI: Python continues to lead because of its easy-to-read style and the huge number of libraries available for tasks from data work to artificial intelligence. Tools like TensorFlow and PyTorch make it a must-have. Whether you’re experienced or just starting, Python’s clear style makes it a good choice for diving into machine learning. Actionable Tip: If you’re new to Python,... - Source: dev.to / 3 months ago
  • 🚀 Launching a High-Performance DistilBERT-Based Sentiment Analysis Model for Steam Reviews 🎮🤖
    Scikit-learn (optional): Useful for additional training or evaluation tasks. - Source: dev.to / 5 months ago
  • Essential Deep Learning Checklist: Best Practices Unveiled
    How to Accomplish: Utilize data splitting tools in libraries like Scikit-learn to partition your dataset. Make sure the split mirrors the real-world distribution of your data to avoid biased evaluations. - Source: dev.to / 11 months ago
  • How to Build a Logistic Regression Model: A Spam-filter Tutorial
    Online Courses: Coursera: "Machine Learning" by Andrew Ng EdX: "Introduction to Machine Learning" by MIT Tutorials: Scikit-learn documentation: https://scikit-learn.org/ Kaggle Learn: https://www.kaggle.com/learn Books: "Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow" by Aurélien Géron "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman By... - Source: dev.to / about 1 year ago
  • Link Prediction With node2vec in Physics Collaboration Network
    Firstly, we need a connection to Memgraph so we can get edges, split them into two parts (train set and test set). For edge splitting, we will use scikit-learn. In order to make a connection towards Memgraph, we will use gqlalchemy. - Source: dev.to / almost 2 years ago
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What are some alternatives?

When comparing JMP and Scikit-learn, you can also consider the following products

Stata - Stata is a software that combines hundreds of different statistical tools into one user interface. Everything from data management to statistical analysis to publication-quality graphics is supported by Stata. Read more about Stata.

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

GraphPad Prism - Overview. GraphPad Prism, available for both Windows and Mac computers, combines scientific graphing, comprehensive curve fitting (nonlinear regression), understandable statistics, and data organization.

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

RStudio - RStudio™ is a new integrated development environment (IDE) for R.

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

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