astroML
astroml.orgAstroML provides Python tools for machine learning and data mining in astronomy.
Looking for something like mlxtend? Here are 24 alternatives, ranked by how closely they match it on purpose, features and platforms. Of these, 1 are free or have a free plan and 18 are open source.
mlxtend adds helper modules for Python data analysis and machine learning libraries.
| # | Alternative | Pricing | Open source | Platforms |
|---|---|---|---|---|
| 1 | astroML | — | Not stated | Web |
| 2 | DataPrep | — | Yes | — |
| 3 | Streamlit | Free | Yes | — |
| 4 | Microsoft ML for Apache Spark | — | Yes | — |
| 5 | Whylogs | — | Yes | — |
| 6 | Piperider | — | Yes | — |
| 7 | statsmodels | — | Yes | — |
| 8 | PyCM | — | Yes | — |
| 9 | imbalanced-ensemble | — | Yes | — |
| 10 | Hamilton | — | Yes | — |
AstroML provides Python tools for machine learning and data mining in astronomy.
For Python data preparation, DataPrep offers an open-source low-code library to collect, clean, and visualize data.
Python developers can use Streamlit to build and share data applications.
SynapseML provides distributed machine learning algorithms for Spark through its Python library.
Machine learning pipelines can use whylogs, an open-source library for logging data quality and model performance.
PipeRider provided code review tools for dbt data and has been superseded by Recce.
statsmodels provides statistical modeling and econometrics tools in Python.
PyCM provides a Python library for working with multiclass confusion matrices.
Class-imbalanced machine learning is supported by imbalanced-ensemble, a toolbox for ensemble methods and datasets.
Apache Hamilton helps teams build modular, testable dataflows with lineage and metadata.
Python developers can use gensim for topic modeling and related natural language processing tasks.
The Geni project provides a Clojure dataframe library that runs on Apache Spark.
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.
Browse a gallery of interesting Jupyter notebooks on this project wiki, with examples from notebook users.
Exploratory data analysis in Python is the focus of Dora, a library with functions for cleaning, feature selection, visualization, and validation.
For machine learning pipelines, Upgini searches and enriches data with features from public and commercial sources.
MultivariateStats.jl provides Julia tools for multivariate statistics and data analysis, including dimension reduction.
Stan provides software for Bayesian data analysis, including tools for sophisticated statistical modeling.
Accord.NET combines machine learning with audio and image processing libraries in a C# framework.
dlib provides C++ libraries for image processing, machine learning, and related algorithms.
Python developers can use Dash to build data apps and dashboards without requiring JavaScript.
The pandas project provides open-source Python tools for analyzing and manipulating data.
emcee provides a Python toolkit for affine-invariant Markov chain Monte Carlo sampling.
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