mlxtend
github.com/rasbt/mlxtendmlxtend adds helper modules for Python data analysis and machine learning libraries.
Looking for something like astroML? 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 20 are open source.
AstroML provides Python tools for machine learning and data mining in astronomy.
| # | Alternative | Pricing | Open source | Platforms |
|---|---|---|---|---|
| 1 | mlxtend | — | Not stated | — |
| 2 | A gallery of interesting IPython notebooks | — | Yes | — |
| 3 | Dora | — | Yes | — |
| 4 | DataPrep | — | Yes | — |
| 5 | Streamlit | Free | Yes | — |
| 6 | Microsoft ML for Apache Spark | — | Yes | — |
| 7 | Whylogs | — | Yes | — |
| 8 | Pandas | — | Not stated | Web |
| 9 | Vaex | — | Not stated | Web |
| 10 | Piperider | — | Yes | — |
mlxtend adds helper modules for Python data analysis and machine learning libraries.
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 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.
The pandas project provides open-source Python tools for analyzing and manipulating data.
Vaex provides out-of-core DataFrames for Python to explore and visualize large tabular datasets, with support for machine learning workflows.
PipeRider provided code review tools for dbt data and has been superseded by Recce.
emcee provides a Python toolkit for affine-invariant Markov chain Monte Carlo sampling.
MultivariateStats.jl provides Julia tools for multivariate statistics and data analysis, including dimension reduction.
statsmodels provides statistical modeling and econometrics tools in Python.
PyCM provides a Python library for working with multiclass confusion matrices.
The ml5.js project brings friendly machine learning tools to web projects.
The anneal project is a Go machine learning compiler with graph rewriting, autodiff, and a WebGPU backend.
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.
Automatic machine learning, hyperparameter tuning, and pipeline abstractions are provided by the Neuraxle software library.
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.
Sparkling Water brings H2O machine learning functionality into an Apache Spark cluster.
For Go developers, GoLearn provides a collection of tools for machine learning.
Class-imbalanced machine learning is supported by imbalanced-ensemble, a toolbox for ensemble methods and datasets.
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