Effectively Dealing with Imbalance Classes

提供方
Coursera Project Network
在此指导项目中,您将:

Import dataset and perform EDA & visualizations

Become familiar with the variety of under sampling techniques, their advantages & dis-advantages and implement them.

Clock2 Hours
Intermediate中级
Cloud无需下载
Video分屏视频
Comment Dots英语(English)
Laptop仅限桌面

In this 2 hour guided project you will learn how to deal with imbalance classification problems in a profound manner, applying several resampling strategies and visualizing the effects of resampling on imbalance classification dataset. Note: This project works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

您要培养的技能

  • ADASYN
  • SMOTETomek
  • SMOTE
  • Machine Learning
  • Data Visualization (DataViz)

分步进行学习

在与您的工作区一起在分屏中播放的视频中,您的授课教师将指导您完成每个步骤:

  1. Task 1: Importing data, Exploratory data analysis & visualizations

  2. Task 2: Applying under sampling strategies: Random & TomekLinks

  3. Task 3: Applying over sampling strategies: SMOTE & SVMSMOTE

  4. Task 4: Combining Over & Under Sampling strategies: SMOTETomek

  5. Task 5: Metrics Discussion & Comparison of impact of all the strategies

指导项目工作原理

您的工作空间就是浏览器中的云桌面,无需下载

在分屏视频中,您的授课教师会为您提供分步指导

常见问题

常见问题

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