Documentation

A library to effortlessly import models trained on different platforms and with programming languages into scikit-learn in Python. First export your model to PMML (widely supported). Next, load the exported PMML file with this library, and use the class as any other scikit-learn estimator.

Install

The easiest way to install sklearn-pmml-model is to use pip by running:

$ pip install sklearn-pmml-model

Alternatively, you can install from source using the details described on GitHub.

API Reference

The reference guide contains a detailed description of the sklearn-pmml-model API. It describes which classes and functions are available along with their arguments.

Contents:

• Install
  • pip
  • From source
  • Dependencies
• Contribute
  • Scope of this package
  • Reporting bugs
  • Get a local copy
  • Setting up a development environment
  • Making changes to the code
  • Submitting a Pull Request
• Release notes
  • 1.0.7
  • 1.0.6
  • 1.0.5
  • 1.0.4
  • 1.0.3
  • 1.0.2
  • 1.0.1
  • 1.0.0
  • 0.0.22
  • 0.0.21
  • 0.0.20
  • 0.0.19
  • 0.0.18
  • 0.0.17
  • 0.0.16
  • 0.0.15
  • 0.0.14.1
  • 0.0.14
  • 0.0.13
  • 0.0.12
  • 0.0.11
  • 0.0.10
• API Reference
  • sklearn_pmml_model
    • Subpackages
      • sklearn_pmml_model.auto_detect
      • sklearn_pmml_model.ensemble
      • sklearn_pmml_model.linear_model
      • sklearn_pmml_model.naive_bayes
      • sklearn_pmml_model.neighbors
      • sklearn_pmml_model.neural_network
      • sklearn_pmml_model.svm
      • sklearn_pmml_model.tree
    • Submodules
      • sklearn_pmml_model.base
      • sklearn_pmml_model.datatypes
    • Package Contents
      • __version__