Related Projects¶
Below is a list of sister-projects, extensions and domain specific packages.
Interoperability and framework enhancements¶
These tools adapt scikit-learn for use with other technologies or otherwise enhance the functionality of scikit-learn’s estimators.
- sklearn_pandas bridge for scikit-learn pipelines and pandas data frame with dedicated transformers.
- Scikit-Learn Laboratory A command-line wrapper around scikit-learn that makes it easy to run machine learning experiments with multiple learners and large feature sets.
- auto-sklearn An automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator
- sklearn-pmml Serialization of (some) scikit-learn estimators into PMML.
Other estimators and tasks¶
Not everything belongs or is mature enough for the central scikit-learn project. The following are projects providing interfaces similar to scikit-learn for additional learning algorithms, infrastructures and tasks.
- pylearn2 A deep learning and neural network library build on theano with scikit-learn like interface.
- sklearn_theano scikit-learn compatible estimators, transformers, and datasets which use Theano internally
- lightning Fast state-of-the-art linear model solvers (SDCA, AdaGrad, SVRG, SAG, etc...).
- Seqlearn Sequence classification using HMMs or structured perceptron.
- HMMLearn Implementation of hidden markov models that was previously part of scikit-learn.
- PyStruct General conditional random fields and structured prediction.
- py-earth Multivariate adaptive regression splines
- sklearn-compiledtrees Generate a C++ implementation of the predict function for decision trees (and ensembles) trained by sklearn. Useful for latency-sensitive production environments.
- lda: Fast implementation of Latent Dirichlet Allocation in Cython.
- Sparse Filtering Unsupervised feature learning based on sparse-filtering
- Kernel Regression Implementation of Nadaraya-Watson kernel regression with automatic bandwidth selection
- gplearn Genetic Programming for symbolic regression tasks.
- nolearn A number of wrappers and abstractions around existing neural network libraries
- sparkit-learn Scikit-learn functionality and API on PySpark.
- keras Theano-based Deep Learning library.
- mlxtend Includes a number of additional estimators as well as model visualization utilities.
Statistical learning with Python¶
Other packages useful for data analysis and machine learning.
- Pandas Tools for working with heterogeneous and columnar data, relational queries, time series and basic statistics.
- theano A CPU/GPU array processing framework geared towards deep learning research.
- Statsmodel Estimating and analysing statistical models. More focused on statistical tests and less on prediction than scikit-learn.
- PyMC Bayesian statistical models and fitting algorithms.
- REP Environment for conducting data-driven research in a consistent and reproducible way
- Sacred Tool to help you configure, organize, log and reproduce experiments
- gensim A library for topic modelling, document indexing and similarity retrieval
- Seaborn Visualization library based on matplotlib. It provides a high-level interface for drawing attractive statistical graphics.
- Deep Learning A curated list of deep learning software libraries.
Domain specific packages¶
- scikit-image Image processing and computer vision in python.
- Natural language toolkit (nltk) Natural language processing and some machine learning.
- NiLearn Machine learning for neuro-imaging.
- AstroML Machine learning for astronomy.
- MSMBuilder Machine learning for protein conformational dynamics time series.