{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", " \n", "## [mlcourse.ai](https://mlcourse.ai) – Open Machine Learning Course \n", "###
Author: Aleksandr Korotkov, ODS Slack krotix\n", " \n", "##
Tutorial\n", "###
Yet another ensemble learning helper" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![title](https://static1.squarespace.com/static/57dc396a03596e8da9fe6b73/t/57eef283b3db2ba633355a07/1480477568336/UBC_Bands.jpg)\n", "
Image by Brian Hawkes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### What does ensemble learning mean?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Ensemble learning** - this is a method use multiple learning algorithms to obtain(I'd say usually it so, but not in any cases) better predictive performance than could be obtained from any of the constituent learning algorithms alone.\n", "\n", "The most common techniques are:\n", "* boosting\n", "* bagging\n", "* stacking\n", "\n", "We are looking at some meta-algorithms to make an ensemble, which can improve the performance of a metric, get a better speed of experimenting and simplify code.\n", "\n", "I would like to show several libraries for ensambling in python:\n", "* https://github.com/rasbt/mlxtend.git - a library of useful tools for the day-to-day data science tasks.\n", "* https://github.com/flennerhag/mlens - a library for high performance ensemble learning.\n", "* https://github.com/Menelau/DESlib - an easy-to-use ensemble learning library focused on the implementation of the state-of-the-art techniques for dynamic classifier and ensemble selection.\n", "\n", "We will understand the use of different libraries on a simple example and plot of decision boundaries to visualize differences." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###