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scikit-learn user guide, Release 0.22.dev0
• TPOT An automated machine learning toolkit that optimizes a series of scikit-learn operators to design a ma-
chine learning pipeline, including data and feature preprocessors as well as the estimators. Works as a drop-in
replacement for a scikit-learn estimator.
• scikit-optimize A library to minimize (very) expensive and noisy black-box functions. It implements sev-
eral methods for sequential model-based optimization, and includes a replacement for GridSearchCV or
RandomizedSearchCV to do cross-validated parameter search using any of these strategies.
Experimentation frameworks
• REP Environment for conducting data-driven research in a consistent and reproducible way
• ML Frontend provides dataset management and SVM fitting/prediction through web-based and programmatic
interfaces.
• 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.
• Xcessiv is a notebook-like application for quick, scalable, and automated hyperparameter tuning and stacked
ensembling. Provides a framework for keeping track of model-hyperparameter combinations.
Model inspection and visualisation
• eli5 A library for debugging/inspecting machine learning models and explaining their predictions.
• mlxtend Includes model visualization utilities.
• scikit-plot A visualization library for quick and easy generation of common plots in data analysis and machine
learning.
• yellowbrick A suite of custom matplotlib visualizers for scikit-learn estimators to support visual feature analysis,
model selection, evaluation, and diagnostics.
Model export for production
• onnxmltools Serializes many Scikit-learn pipelines to ONNX for interchange and prediction.
• sklearn2pmml Serialization of a wide variety of scikit-learn estimators and transformers into PMML with the
help of JPMML-SkLearn library.
• sklearn-porter Transpile trained scikit-learn models to C, Java, Javascript and others.
• sklearn-compiledtrees Generate a C++ implementation of the predict function for decision trees (and ensembles)
trained by sklearn. Useful for latency-sensitive production environments.
1.4.2 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.
Structured learning
• 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.
• pomegranate Probabilistic modelling for Python, with an emphasis on hidden Markov models.
• sklearn-crfsuite Linear-chain conditional random fields (CRFsuite wrapper with sklearn-like API).
Deep neural networks etc.
• pylearn2 A deep learning and neural network library build on theano with scikit-learn like interface.
10 Chapter 1. Welcome to scikit-learn