mlxtend
github.com/rasbt/mlxtendmlxtend adds helper modules for Python data analysis and machine learning libraries.
Looking for something like Microsoft ML for Apache Spark? Here are 24 alternatives, ranked by how closely they match it on purpose, features and platforms. Of these, 20 are open source.
SynapseML provides distributed machine learning algorithms for Spark through its Python library.
| # | Alternative | Pricing | Open source | Platforms |
|---|---|---|---|---|
| 1 | mlxtend | — | Not stated | — |
| 2 | Whylogs | — | Yes | — |
| 3 | imbalanced-ensemble | — | Yes | — |
| 4 | Geni | — | Yes | — |
| 5 | gensim | — | Yes | — |
| 6 | jSciPy | — | Yes | Android |
| 7 | Weights & Biases | — | Yes | — |
| 8 | H2O Sparkling Water | — | Yes | — |
| 9 | Upgini | — | Yes | — |
| 10 | DataComPy | — | Yes | — |
mlxtend adds helper modules for Python data analysis and machine learning libraries.
Machine learning pipelines can use whylogs, an open-source library for logging data quality and model performance.
Class-imbalanced machine learning is supported by imbalanced-ensemble, a toolbox for ensemble methods and datasets.
The Geni project provides a Clojure dataframe library that runs on Apache Spark.
Python developers can use gensim for topic modeling and related natural language processing tasks.
jSciPy provides Java scientific computing tools for signal processing, FFT, filtering, power spectral density, and DCT.
Weights & Biases supports model training, fine tuning, and management from experimentation through production.
Sparkling Water brings H2O machine learning functionality into an Apache Spark cluster.
For machine learning pipelines, Upgini searches and enriches data with features from public and commercial sources.
DataComPy compares Pandas, Polars, Spark, and Snowpark dataframes, with output that includes comparison statistics.
Koalas implements the pandas DataFrame API on Apache Spark and is deprecated; for Apache Spark 3.2 and above, use PySpark directly.
dlib provides C++ libraries for image processing, machine learning, and related algorithms.
AstroML provides Python tools for machine learning and data mining in astronomy.
Vaex provides out-of-core DataFrames for Python to explore and visualize large tabular datasets, with support for machine learning workflows.
Automatic machine learning, hyperparameter tuning, and pipeline abstractions are provided by the Neuraxle software library.
For Python data preparation, DataPrep offers an open-source low-code library to collect, clean, and visualize data.
The ml5.js project brings friendly machine learning tools to web projects.
The Pattern Recognition Toolbox provides MATLAB tools for pattern recognition and machine learning.
Netron displays neural network, deep learning, and machine learning models across formats such as ONNX, TensorFlow, and PyTorch.
ONNX-Scala provides an API and backend for deep and classical machine learning in Scala.
Opyrator turns machine learning code into microservices with a web API and interactive interface.
The anneal project is a Go machine learning compiler with graph rewriting, autodiff, and a WebGPU backend.
For Go developers, GoLearn provides a collection of tools for machine learning.
Python developers can use Dash to build data apps and dashboards without requiring JavaScript.
Alternatives are suggested from the purpose and features described in our listings. Different pages for the same product appear once. Read each description to check whether it fits your task. How we choose alternatives · Suggest an alternative