地球资源数据云——数据资源详情
NLP - SPAM/HAM 电子邮件分类
该数据集《NLP - SPAM/HAM Email Classification》主要用于二分类任务，数据形态以文本为主，应用场景偏向文本内容分析。 题目说明：SPAM or HAM(legitimate) Email Classification 任务类型：文本二分类。 建议流程：先做文本清洗与分词，再比较 TF - IDF+线性模型 与 预训练语言模型。 评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。 可用文件：emails.csv。 The dataset and working belongs to getting started in NLP Content: - The dataset contains email received. The dataset contains two columns Text and Spam. Text : - Text / Email received. Spam: - Whether the email received is spam/ham(legitimate) Class 0: HAM (Legitimate) Class 1: SPAM Goal: - Using NLP we have to build a model for future mails that can classify into spam/ham
摘要概览
该数据集《NLP - SPAM/HAM Email Classification》主要用于二分类任务，数据形态以文本为主，应用场景偏向文本内容分析。 题目说明：SPAM or HAM(legitimate) Email Classification
任务类型：文本二分类。
建议流程：先做文本清洗与分词，再比较 TF - IDF+线性模型 与 预训练语言模型。
评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。
可用文件：emails.csv。
The dataset and working belongs to getting started in NLP Content: - The dataset contains email received. The dataset contains two columns Text and Spam.
Text : - Text / Email received.
Spam: - Whether the email received is spam/ham(legitimate) Class 0: HAM (Legitimate) Class 1: SPAM
Goal: - Using NLP we have to build a model for future mails that can classify into spam/ham