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scikit-learn

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950
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SciPy

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scikit-learn vs SciPy: What are the differences?

Developers describe scikit-learn 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. On the other hand, SciPy is detailed as "Scientific Computing Tools for Python". Python-based ecosystem of open-source software for mathematics, science, and engineering. It contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering.

scikit-learn can be classified as a tool in the "Machine Learning Tools" category, while SciPy is grouped under "Data Science Tools".

scikit-learn and SciPy are both open source tools. It seems that scikit-learn with 36K GitHub stars and 17.6K forks on GitHub has more adoption than SciPy with 6.01K GitHub stars and 2.85K GitHub forks.

According to the StackShare community, scikit-learn has a broader approval, being mentioned in 71 company stacks & 40 developers stacks; compared to SciPy, which is listed in 12 company stacks and 4 developer stacks.

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Pros of scikit-learn
Pros of SciPy
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    Scientific computing
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    Cons of scikit-learn
    Cons of SciPy
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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 SciPy?

      Python-based ecosystem of open-source software for mathematics, science, and engineering. It contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering.

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

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      See which teams inside your own company are using scikit-learn or SciPy.
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      What tools integrate with scikit-learn?
      What tools integrate with SciPy?

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      What are some alternatives to scikit-learn and SciPy?
      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