Effectively Dealing with Imbalance Classes

提供方
Coursera Project Network
在此指導項目中,您將:

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中級
Cloud無需下載
Video分屏視頻
Comment Dots英語(English)
Laptop僅限桌面

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.

您要培養的技能

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

分步進行學習

在與您的工作區一起在分屏中播放的視頻中,您的授課教師將指導您完成每個步驟:

  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

指導項目工作原理

您的工作空間就是瀏覽器中的雲桌面,無需下載

在分屏視頻中,您的授課教師會為您提供分步指導

常見問題

常見問題

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