Data Science With R Course Series – Week 5

The culmination of the previous weeks has been preparation for machine learning modeling. At this stage, we have defined the business problem, we’ve explored and understood how the data relates to the business problem, and we’ve preprocessed the data in preparation for modeling.

This week will focus on the following topics:

• Introduction to automated machine learning, predictive modeling with H2O
• Apply automated machine learning to the Attrition problem
• Develop high-performance models and learn how automated machine learning works
• Develop a visualization of the models to communicate the best and worst performing models

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

Recap: Data Science With R Course Series

You’re in the Week 5: Predictive Modeling With H2O. 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 1: Getting Started
• Week 2: Business Understanding
• Week 3: Data Understanding
• Week 4: Data Preparation
• Week 5: Predictive Modeling With H2O (You’re Here)
• Week 6: H2O Model Performance
• Week 7: Machine Learning Interpretability With LIME
• Week 8: Link Data Science To Business With Expected Value
• Week 9: Expected Value Optimization And Sensitivity Analysis
• Week 10: Build A Recommendation Algorithm To Improve Decision Making

Week 5: Predictive Modeling With H2O

Student Feedback

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Week 5: Predictive Modeling With H2O

Modeling Setup

The modeling setup focuses on structuring your project to support new utility functions and extracted models from H2O automated machine learning.

In addition, you will be introduced to the new libraries cowplot, fs and glue – used in evaluating H2O model performance.

H2O Automated Machine Learning

H2O automated machine learning is an exciting part of the course. This module will teach you the following:

• Learn how to use the H2O library to create machine learning models
• Inspect the model leaderboard to select the best performing models
• Extract and save models to your directory structure for exploration
• Make predictions using your models
• Use H2O documentation to continue learning

Advanced Concepts

Automated machine learning is an effective way to create and compare multiple machine learning models. However, you still need to understand why a model is good or bad and adjust individual model parameters for better performance.

The advanced concepts will teach you how to modify model parameters, create a stable model using k-fold cross-validation, and an introduction to the grid search algorithm – the process automated machine learning uses to generate models.

Visualizing The Leaderboard

Leaderboard visualization is a great way to communicate model performance. Learn how to create a function for visual representation of your machine learning models. The visualization can be filtered by model metrics, such as AUC or Logloss (more about AUC and Logloss in week 6).

Bonus! Grid Search In H2O

The Grid Search in H2O Bonus continues the model performance discussion from Advanced Concepts! In this module you will learn how to tune a model that was generated from automated machine learning.

Learn the details of extracting a model, analyzing performance metrics, and more!

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Next Up

The next article in the Data Science With R Series covers H2O Model Performance.

Week 6 will cover H2O model performance and communicating performance to different stakeholders. You will learn how to:

1. Analyze H2O performance with precision and recall
2. Create a performance chart for data scientists
3. Create a performance chart for business people
4. Create the ultimate model performance dashboard

Week 6: H2O Model Performance

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