{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "VpyZm2kikUxY" }, "source": [ "# League of Legends Win Prediction with XGBoost\n", "\n", "This notebook uses the Kaggle dataset [League of Legends Ranked Matches](https://www.kaggle.com/paololol/league-of-legends-ranked-matches) which contains 180,000 ranked games of League of Legends starting from 2014. Using this data we build an XGBoost model to predict if a player's team will win based on statistics about how that player played the match.\n", "\n", "The methods used here are applicable to any dataset. We use this dataset to illustrate how SHAP values help make gradient boosted trees such as XGBoost interpretable. Due to the size, interaction effections, containing both catgoriacl and continuous features and its interpretability (particularly for players of the game) the dataset suits as a good example on various fronts. For more information on SHAP values see: https://github.com/shap/shap " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": {}, "colab_type": "code", "id": "cdWasF7PkUxa", "outputId": "e9ee993f-2aba-4d88-9045-e617f82e500b" }, "outputs": [ { "data": { "text/html": [ "