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The official Jupyter Conference
August 22-23, 2017: Training
August 23-25, 2017: Tutorials & Conference
New York, NY

Model interpretation guidelines for the enterprise: Using Jupyter’s interactiveness to build better predictive models (sponsored by

Pramit Choudhary (
11:05am–11:45am Friday, August 25, 2017
Sponsored, Usage and application
Location: Regent Parlor Level: Intermediate

Who is this presentation for?

  • Data-scientists, Machine-learning practitioners, Data Analysts, data-engineers, product managers involved with analytical/predictive modeling workflows

Prerequisite knowledge

  • Basic familiarity with machine learning concepts, and analytical workflow.

What you'll learn

  • Understand how Jupyter’s interactive nature contributes to building effective predictive Models
  • Understanding importance of human in the loop: Highlights best practices on effective ways to understands Model’s behavior while building predictive modeling pipelines
  • Better understanding of the concept of Model Evaluation and Interpretability as it relates to enterprise data challenges


The adoption of machine learning and statistical models to solve real-world problems has increased exponentially, but users still struggle to derive the full potential of the predictive models. There is a dichotomy between explainability and model performance while making the choice of the algorithm. Linear models and simple decision trees are often preferred over more complex models such as ensembles or deep learning when operationalizing models for ease of interpretation, which often results in a loss of accuracy. But is it necessary to accept a trade-off between model complexity and interpretability?

Being able to faithfully interpret a model globally, using partial dependence plots (PDP) and relative feature importance, and locally, using local interpretable model-agnostic interpretation (LIME), helps in understanding feature contribution on predictions and model variability in a nonstationary environment. This enables trust in the algorithm, which drives better collaboration and communication among peers. And the need to understand the variability in the predictive power of a model in human-interpretable way is even more important for complex models (e.g., text, images, and machine translations).

Pramit Choudhary offers an overview of’s model interpretation library Skater, explains how to use it to evaluate models using the Jupyter environment, and shares how it could help analysts, data scientists, and statisticians better understand their model behavior—without compromising on the choice of algorithm.

This session is sponsored by

Photo of Pramit Choudhary

Pramit Choudhary

Pramit Choudhary is a Lead data scientist/ML scientist at, where he focuses on optimizing and applying classical machine learning and Bayesian design strategy to solve large scale real-world problems.
Currently, he is leading initiatives on figuring out better ways to generate a predictive model’s learned decision policies as meaningful insights(Supervised/Unsupervised problems)