{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "provenance": [] }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" } }, "cells": [ { "cell_type": "markdown", "source": [ "## Supervised Learning - Logistic Regression" ], "metadata": { "id": "yZdZqJiQZ3zZ" } }, { "cell_type": "markdown", "metadata": { "id": "heV7Rn3KNyhZ" }, "source": [ "\n", "\n", "### What is *classification*? What is the difference between *regression* and *classification*?\n", "\n", "**Regression**: the dependent variable is continuous and we want to predict the expected value given the input features.\n", "\n", "**Classification**: the dependent variable is binary or nominal and we want to predict the corect class given the input features.\n", "\n", "If we had one input feature as a continuous variable we could ***see*** the classification.\n", "\n", "**Example** Let's imagine we have data for the weights of two different animals and we would like to know whether the *weight* alone may be a good predictor for what type of animal there is.\n", "\n", "\n", "\n", "\n", "\n", "
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