Human Interpretable Machine Learning  — The Need and Importance of Model Interpretation (with hands-on examples)

The field of Machine Learning has gone through some phenomenal changes over the last decade. In the industry, the main focus of data science or machine learning is more ‘applied’ rather than theoretical and effective application of these models on the right data to solve complex real-world problems is of paramount importance.

A machine learning model by itself consists of an algorithm which tries to learn latent patterns and relationships from data without hard-coding fixed rules. Hence, explaining how a model works to the business always poses its own set of challenges. In this talk, I will be covering the need and importance of human interpretable machine learning approaches, look at effective strategies for model interpretation and several hands-on examples. Detailed coverage of open-source frameworks for machine learning model interpretation will also be one of the major focus areas. Examples will be showcased in Python.

 
 

Outline/Structure of the Talk

Part 1: The Need and Importance of Model Interpretation

  • Understanding Model Interpretation
  • Importance of Model Interpretation
  • Criteria for Model Interpretation Methods
  • Scope of Model Interpretation

Part 2: Model Interpretation Techniques

  • Model Interpretation Strategies
  • Existing Techniques for Model Interpretation
  • Challenges and Limitations of Existing Techniques
  • Strategies for combating the accuracy vs. interpretability trade-off

Part 3: Hands-on Model Interpretation

  • Introducing and Understanding Skater
  • Hands-on Machine Learning Model Interpretation with Skater
  • Model interpretation for regression and classification problems

Part 4: Hands-on Advanced Model Interpretation

  • Hands-on Model Interpretation on Unstructured Data (text) if time permits.

Learning Outcome

  • Understand the need of model interpretation in the real-world
  • Learn about present gaps between data science and business stakeholders
  • Learn about present techniques and limitations around model evaluation
  • Deep dive into effective model interpretation strategies
  • Learn about popular open-source model interpretation frameworks
  • Get a detailed perspective on how model interpretation is used with hands-on examples

Target Audience

Data Scientists, Data Enthusiasts, Data Analysts, Managers & Anyone with an interest in Machine Learning

Prerequisites for Attendees

Basic concepts around machine learning like models, performance evaluation techniques would help. However we will be covering them during the talk so it is not compulsory.

schedule Submitted 1 year ago

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