Data Science With R Course Series - Week 6

Written by David Curry on October 22, 2018





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Welcome to week 6 of the Data Science with R Course Series.

This week’s curriculum is an extension of week 5, where we started analyzing model performance.

This week will cover the following:

  • Learn techniques to communicate and measure model performance
  • Learn how to analyze individual machine learning model metrics
  • Learn how to visually compare multiple models based on metrics
  • Learn how to communicate model performance to different stakeholders


Here is a recap of our trajectory and the course overview:

Recap: Data Science With R Course Series

You’re in the Week 6: H2O Model Performance. Here’s our game-plan over the 10 articles in this series. We’ll cover how to apply data science for business with R following our systematic process.

Week 6: H2O Model Performance


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Week 6: H2O Model Performance

Performance Overview & Setup

The Performance Overview & Setup module summarizes this week’s course material and reviews the required code, such as extracted H2O machine learning models and utility script files.


H2O Performance For Binary Classification

Learn how to create an H2O performance object to retrieve model metrics, such as AUC and logloss. Each H2O auto-generated machine learning model can be extracted and saved to memory.

Once the machine learning models are saved, you will learn how to:

  • Compare two competing metrics in binary classification
  • Compare top performing models using the AUC metric


Performance Charts For Data Scientists

In Performance Charts For Data Scientists, take your knowledge on how to measure the performance of a single classifier and create a plot that will measure the performance of multiple models.

Performance charting is a great way for data scientists to evaluate model performance using different metrics.



Performance Charts For Business People

All your efforts to this point have been to create a high-performance model that accurately predicts employee churn. However, business people will not be receptive to the language of data science.

In this module, learn how to create a chart for business people to understand how much a model will improve results. The performance chart for business people will help communicate what can be done to solve the problem and understand how serious the problem is.


Ultimate Model Performance Comparison Dashboard

In this lecture, you will create a model metrics dashboard that combines the performance chart for data scientists and performance chart for business people into one visualization. Combining the charts assist in evaluating the strengths and weaknesses of multiple models in one view.




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Next Up

The next article in the Data Science With R Series covers Modeling Churn: Explaining Black-Box Models With LIME.

Week 7 will cover feature explanation using a the R library Lime. Prediction explanation is important as a data scientist in order to communicate model results. Using Lime, you will learn how to explain your machine learning model and empower business people to make better decisions.

Week 7: Modeling Churn: Explaining Black-Box Models With LIME



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DS4B 301-R Shiny Application: Employee Prediction

Building an R + Shiny Web App, DS4B 301-R


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Data Science for Business Curriculum

Business Science University is an educational platform that teaches how to apply data science to business. Our offering includes of a fully integrated, project-based 3-Course R-Track.


BSU R-Track Course Curriculum


Each course takes the student through their progression in a data science journey. Begin your journey with DS4B 101-R which teaches foundations using the tidyverse. Next, master machine learning for business with DS4B 201-R, where you learn H2O and many advanced R packages. Finish with DS4B 301-R where you learn to develop high-performing web applications using Shiny, a powerful framework for productionizing R code.

R-Track Curriculum Summary

Business Analysis with R (Beginner) - Data Science Foundations 7-Week course 12 tidyverse Packages 2 business projects
Data Science For Business with R (Intermediate/Advanced) - Machine Learning + Business Consulting 10-Week course H2O, LIME, recipes, and 10 more packages 1 end-to-end business project
Web Apps for Business with Shiny (Advanced) - Web Frameworks (Bootstrap, HTML/CSS) and Shiny 6-Week course Shiny, shinytest, shinyloadtest, profvis, and more! Take machine learning model into production

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