Delete question.ipynb

人工智能系统实战第三期/实战代码/基于集成学习的Amazon用户评论质量预测/question.ipynb

-{
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 基于集成学习的 Amazon 用户评论质量预测\n",
-    "\n",
-    "## 案例简介\n",
-    "在进行线上商品挑选时，评论往往是我们十分关注的一个方面。然而目前电商网站的评论质量参差不齐，甚至有水军刷好评或者恶意差评的情况出现，严重影响了顾客的购物体验。因此，对于评论质量的预测成为电商平台越来越关注的话题，如果能自动对评论质量进行评估，就能根据预测结果避免展现低质量的评论。本案例中我们将基于集成学习的方法对Amazon现实场景中的评论质量进行预测。\n",
-    "\n",
-    "## 作业说明\n",
-    "本案例中需要大家完成两种集成学习算法的实现（Bagging、AdaBoost），其中基分类器要求使用 SVM 和决策树两种，因此，一共需要对比四组结果（AUC 作为评价指标）：\n",
-    "1.Bagging + SVM\n",
-    "\n",
-    "2.Bagging + 决策树\n",
-    "\n",
-    "3.AdaBoost + SVM\n",
-    "\n",
-    "4.AdaBoost + 决策树\n",
-    "\n",
-    "注意集成学习的核心算法需要手动进行实现，基分类器可以调库。\n",
-    "\n",
-    "### 基本作业（80分）\n",
-    "1.根据数据格式设计特征的表示\n",
-    "\n",
-    "2.汇报不同组合下得到的 AUC\n",
-    "\n",
-    "3.结合不同集成学习算法的特点分析结果之间的差异\n",
-    "\n",
-    "（使用 sklearn 等第三方库的集成学习算法会酌情扣分）\n",
-    "\n",
-    "### 扩展作业（20分）\n",
-    "1.尝试其他基分类器（如 k-NN、朴素贝叶斯，神经网络）分析不同特征的影响\n",
-    "\n",
-    "2.分析集成学习算法参数的影响"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "train_df = pd.read_csv('train.xlsx', sep='\\t')\n",
-    "test_df = pd.read_csv('test.xlsx', sep='\\t',index_col=False)\n",
-    "train_df.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "分析数据集\n",
-    "\n",
-    "reviewID是用户ID\n",
-    "\n",
-    "asin是商品ID\n",
-    "\n",
-    "reviewText是评论内容\n",
-    "\n",
-    "overall是用户对商品的打分\n",
-    "\n",
-    "votes_up是认为评论有用的点赞数\n",
-    "\n",
-    "votes_all是该评论得到的总点赞数\n",
-    "\n",
-    "label是标签"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "pytorch_gpu",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "name": "python",
-   "version": "3.11.3"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+++ /dev/null