DataHour: Dealing with Imbalanced Datasets in ML Classification Problems

DataHour: Dealing with Imbalanced Datasets in ML Classification Problems

20 Feb 202313:02pm - 20 Feb 202314:02pm

DataHour: Dealing with Imbalanced Datasets in ML Classification Problems

About the Event

An Imbalanced Classification Problem is an example of a classification problem where the classes of the response are biased or skewed. Imbalanced datasets pose a challenge for predictive modeling as most of the machine learning algorithms used for classification were designed around the assumption of an equal number of examples for each class. This results in models that have poor predictive performance. 

In this DataHour, Damini will explore the following topics in detail:

  • What are highly imbalanced datasets and the problems associated around them
  • Identifying the right metrics to use in case of imbalanced classification
  • How to treat imbalanced datasets to improve your model accuracy


Prerequisites: 
Enthusiasm of learning AI and ML.

  1. Best articles get published on Analytics Vidhya’s Blog Space
  2. Best articles get published on Analytics Vidhya’s Blog Space
  3. Best articles get published on Analytics Vidhya’s Blog Space
  4. Best articles get published on Analytics Vidhya’s Blog Space
  5. Best articles get published on Analytics Vidhya’s Blog Space

Who is this DataHour for?

  1. Best articles get published on Analytics Vidhya’s Blog Space
  2. Best articles get published on Analytics Vidhya’s Blog Space
  3. Best articles get published on Analytics Vidhya’s Blog Space

About the Speaker

Damini Dasgupta

Damini Dasgupta

Data Science Professional working at a leading American Bank

Damini is a Data Science professional, currently working with a leading American Bank. She has experience in BFSI and FMCG sectors, and has a consulting background with one of the Big 4 companies. She pursued her under-graduate and postgraduate degrees in statistics, from St. Xavier's College (Kolkata) and Lady Shri Ram College (Delhi University), respectively. In her 2 years of work experience, she has worked on a variety of projects which have reinforced her love and passion towards building a career in Data Science.

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