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scikit-learn

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988
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TensorFlow.js

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scikit-learn vs TensorFlow.js: What are the differences?

What is scikit-learn? 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.

What is TensorFlow.js? Machine Learning in JavaScript. Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API.

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

scikit-learn and TensorFlow.js are both open source tools. It seems that scikit-learn with 36K GitHub stars and 17.6K forks on GitHub has more adoption than TensorFlow.js with 11.2K GitHub stars and 816 GitHub forks.

Repro, Home61, and MonkeyLearn are some of the popular companies that use scikit-learn, whereas TensorFlow.js is used by 8villages, ADEXT, and Taralite. scikit-learn has a broader approval, being mentioned in 71 company stacks & 40 developers stacks; compared to TensorFlow.js, which is listed in 5 company stacks and 3 developer stacks.

Decisions about scikit-learn and TensorFlow.js

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.

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Pros of scikit-learn
Pros of TensorFlow.js
  • 20
    Scientific computing
  • 16
    Easy
  • 5
    Open Source
  • 5
    NodeJS Powered
  • 2
    Deploy python ML model directly into javascript
  • 1
    Privacy - no data sent to server
  • 1
    Can run TFJS on backend, frontend, react native, + IOT
  • 1
    Runs Client Side on device
  • 1
    Easy to share and use - get more eyes on your research
  • 1
    Cost - no server needed for inference

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Cons of scikit-learn
Cons of TensorFlow.js
  • 1
    Limited
    Be the first to leave a con

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    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.

    What is TensorFlow.js?

    Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API

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

    What companies use scikit-learn?
    What companies use TensorFlow.js?
    See which teams inside your own company are using scikit-learn or TensorFlow.js.
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    What tools integrate with scikit-learn?
    What tools integrate with TensorFlow.js?

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    What are some alternatives to scikit-learn and TensorFlow.js?
    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/
    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.
    XGBoost
    Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow
    Apache Spark
    Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
    See all alternatives