{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Adaptive Boosting (AdaBoost)\n", "\n", "In this notebook, we present the Adaptive Boosting (AdaBoost) algorithm. The\n", "aim is to get intuitions regarding the internal machinery of AdaBoost and\n", "boosting in general.\n", "\n", "We will load the \"penguin\" dataset. We will predict penguin species from the\n", "culmen length and depth features." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "\n", "penguins = pd.read_csv(\"../datasets/penguins_classification.csv\")\n", "culmen_columns = [\"Culmen Length (mm)\", \"Culmen Depth (mm)\"]\n", "target_column = \"Species\"\n", "\n", "data, target = penguins[culmen_columns], penguins[target_column]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
Note
\n", "If you want a deeper overview regarding this dataset, you can refer to the\n", "Appendix - Datasets description section at the end of this MOOC.
\n", "