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| 1 | +{ |
| 2 | + "cells": [ |
| 3 | + { |
| 4 | + "cell_type": "markdown", |
| 5 | + "metadata": {}, |
| 6 | + "source": [ |
| 7 | + "# Ensemble Method\n", |
| 8 | + "* Main cause of error while learning are due to noise, bias and variance \n", |
| 9 | + "* minimize above factors \n", |
| 10 | + "* group of weak learners combined to form a strong learner" |
| 11 | + ] |
| 12 | + }, |
| 13 | + { |
| 14 | + "cell_type": "markdown", |
| 15 | + "metadata": {}, |
| 16 | + "source": [ |
| 17 | + "Models are different based on four criteria:\n", |
| 18 | + "1. Difference in population\n", |
| 19 | + "2. Difference in hypothesis\n", |
| 20 | + "3. Difference in modeling technique\n", |
| 21 | + "4. Difference in initial seed" |
| 22 | + ] |
| 23 | + }, |
| 24 | + { |
| 25 | + "cell_type": "markdown", |
| 26 | + "metadata": {}, |
| 27 | + "source": [ |
| 28 | + "**Bagging**\n", |
| 29 | + "Goal is to minimize variance. It creates several subsets of data chosen randomly with replacement. Each subset data is used to train. Average of all predictions are taken " |
| 30 | + ] |
| 31 | + }, |
| 32 | + { |
| 33 | + "cell_type": "markdown", |
| 34 | + "metadata": {}, |
| 35 | + "source": [ |
| 36 | + "**Boosting** \n", |
| 37 | + "Aims at fitting sequentially weak learners in a adaptive way. Each model learns from the mistake of previous model and minimize error. \n", |
| 38 | + "\n", |
| 39 | + "High bias model are computationally less expensive to fit. Once weak learners are chosen, there are two way they can be sequentially fitted. \n", |
| 40 | + "Two Important algo: Adaboost and Gradient Boosting \n", |
| 41 | + "These two algo differ on how they create and aggregate the weak learners during the sequential process. \n", |
| 42 | + "Adaboost: updates the weights attached to each of the training dataset observations \n", |
| 43 | + "Gradient Boosting: updates the value of these observations " |
| 44 | + ] |
| 45 | + }, |
| 46 | + { |
| 47 | + "attachments": {}, |
| 48 | + "cell_type": "markdown", |
| 49 | + "metadata": {}, |
| 50 | + "source": [ |
| 51 | + "<img src=\"Image/ensemble.JPG\" width=\"800\" />" |
| 52 | + ] |
| 53 | + }, |
| 54 | + { |
| 55 | + "cell_type": "code", |
| 56 | + "execution_count": null, |
| 57 | + "metadata": {}, |
| 58 | + "outputs": [], |
| 59 | + "source": [] |
| 60 | + } |
| 61 | + ], |
| 62 | + "metadata": { |
| 63 | + "kernelspec": { |
| 64 | + "display_name": "Python 3", |
| 65 | + "language": "python", |
| 66 | + "name": "python3" |
| 67 | + }, |
| 68 | + "language_info": { |
| 69 | + "codemirror_mode": { |
| 70 | + "name": "ipython", |
| 71 | + "version": 3 |
| 72 | + }, |
| 73 | + "file_extension": ".py", |
| 74 | + "mimetype": "text/x-python", |
| 75 | + "name": "python", |
| 76 | + "nbconvert_exporter": "python", |
| 77 | + "pygments_lexer": "ipython3", |
| 78 | + "version": "3.6.6" |
| 79 | + } |
| 80 | + }, |
| 81 | + "nbformat": 4, |
| 82 | + "nbformat_minor": 2 |
| 83 | +} |
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