Scikit learn documentation

An introduction to machine learning with scikit-learn. The problem solved in supervised learning. Choosing the right estimator¶. Often the hardest part of. Machine Learning in Python.

Simple and efficient tools for data mining and data analysis. Accessible to everybody, and reusable in various contexts. Built on NumPy, SciPy, and matplotlib. API The exact API of all functions and classes, as given by the docstrings.

This is the class and function reference of scikit-learn. Please refer to the full user guide for further details, as the class and function raw specifications may not be enough to give full guidelines on their uses. For reference on concepts repeated across the API, see Glossary of Common Terms and API Elements.

Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. It provides a modern, object-oriented library which is both flexible and scalable. RandomState instance or None, optional (default=None). The seed of the pseudo random number generator used when shuffling the data for probability estimates. In other words, you could use grid_search to search for the best batch_size or epochs as well as the model parameters.

Quick Start A very short introduction into machine learning problems and how to solve them using scikit-learn. Introduced basic concepts and conventions. User Guide The main documentation.

This contains an in-depth description of all algorithms and how to apply them. The managed Scikit - learn environment is an Amazon-built Docker container that executes functions defined in the supplied entry_point Python script. Training is started by calling fit() on this Estimator.

This documentation is for scikit-learn version 0. If you use the software, please consider citing scikit-learn. The scikit - learn community goals are to be helpful, welcoming, and effective. The Development Guide has detailed information about contributing code, documentation , tests, and more. Welcome to sknn’s documentation ! Deep neural network implementation without the learning cliff! This library implements multi-layer perceptrons as a wrapper for the powerful pylearnlibrary that’s compatible with scikit-learn for a more user-friendly and Pythonic interface.

Python module for machine learning built on top of SciPy and is distributed under the 3-Clause BSD license. For all supported scikit - learn classifiers and regressors eli5. In general, there are four steps to using SKLL: Get some data in a SKLL-compatible format. Create a small configuration file describing the machine learning experiment you would like to run.

Run that configuration file with run_experiment. Target vector relative to X. Scikit -multilearn offers extensive user documentation. Install an official release.

Read the user docs, learn from recipes constructed on real data or browse the API reference to find a concrete class or function.