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.
scikit-learn is a tool in the Machine Learning Tools category of a tech stack.
scikit-learn is an open source tool with 47.5K GitHub stars and 22.1K GitHub forks. Here’s a link to scikit-learn's open source repository on GitHub
Who uses scikit-learn?
Companies
170 companies reportedly use scikit-learn in their tech stacks, including Delivery Hero, Tokopedia, and Postclick.
Developers
675 developers on StackShare have stated that they use scikit-learn.
scikit-learn Integrations
Jupyter, Keras, Ludwig, Comet.ml, and cnvrg.io are some of the popular tools that integrate with scikit-learn. Here's a list of all 18 tools that integrate with scikit-learn.
Pros of scikit-learn
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Blog Posts
scikit-learn Alternatives & Comparisons
What are some alternatives to scikit-learn?
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.
