The Fifth Elephant 2019

Gathering of 1000+ practitioners from the data ecosystem

Model interpretability

Submitted by Namrata Hanspal (@namrata4) on Apr 15, 2019

Session type: Short talk of 20 mins Status: Rejected

Abstract

The choice we make

Complex machine learning models work very well at prediction and classification tasks but become really hard to interpret. On the other hand simpler models are easier to interpret but less accurate and hence oftentimes we are made to take a call between interpretability and accuracy.

The need to interpret

We live in exciting progressive times, decision making algorithms are everywhere right from influencing our social experiences, the news we see, our finances to our career opportunities . Turning decision making entirely to algorithms raises concerns. How can we be sure the algorithmically curated news doesn’t have a political party bias or job listings don’t reflect a gender or racial bias. This paves a path to fully understand the algorithm in and out.

Outline:

  • What is model interpretability.
  • Why model interpretability is important.
  • Different types of model interpretability
  • Trade off between interpretability and accuracy.
  • Popular model-agnostic libraries and how do they differ
  • Key takeaways

Outline

The choice we make

Complex machine learning models work very well at prediction and classification tasks but become really hard to interpret. On the other hand simpler models are easier to interpret but less accurate and hence oftentimes we are made to take a call between interpretability and accuracy.

The need to interpret

We live in exciting progressive times, decision making algorithms are everywhere right from influencing our social experiences, the news we see, our finances to our career opportunities . Turning decision making entirely to algorithms raises concerns. How can we be sure the algorithmically curated news doesn’t have a political party bias or job listings don’t reflect a gender or racial bias. This paves a path to fully understand the algorithm in and out.

Outline:

  • What is model interpretability.
  • Why model interpretability is important.
  • Different types of model interpretability
  • Trade off between interpretability and accuracy.
  • Popular model-agnostic libraries and how do they differ
  • Key takeaways

Speaker bio

Data Scientist, avid reader & listener, loves engaging in meaningful conversations over a cup of coffee.

https://www.linkedin.com/in/namrata-hanspal-9613b0132/

Slides

https://docs.google.com/presentation/d/1oXIavmxMeKVXrbiaIVou2y7ftzmYhh6dZhErgC9625w/edit?usp=sharing

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