1B. Introduction to Machine Learning with H2O-3 - Regression

In this tutorial, you will learn how to use H2O's XGBoost and Deep Learning algorithms, as well as H2O's grid search to tune hyperparameters for a regression problem.

You can find the Python and R version of the tutorials in the same file, and you can switch between versions by clicking the "Python" or "R" tab. Please note that these tutorials are designed to be completed one version at a time. The H2O Flow tab is provided as complementary content for your personal knowledge. If you want to complete both versions, and you are working on Aquarium, we recommend you do one at a time, as your labs might run out of time.

You can also complete one of the 2 quizzes (Python or R version) to obtain your H2O-3 tutorial badge.

If you have any feedback or question about the tutorial, please post it in the Discussion tab.

Key:

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Objective
Select the "Read" button to begin.
Select the "Read" button to begin. Explore the main goal of this tutorial
Prerequisites
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Select the "Read" button to begin. Find general information about what you need in order to complete this tutorial
Task 1: Initial Setup
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Select the "Read" button to begin. Import the libraries, estimators, and grid search. Initialize your H2O cluster, and import the dataset
Task 2: Regression Concepts
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Select the "Read" button to begin. Learn about the regression concepts that will help you get a better understanding of this tutorial
Task 3: Start Experiment
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Select the "Read" button to begin. Explore your dataset, split it and choose your response and predictors
Task 4: Build an XGBoost Model
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Select the "Read" button to begin. Build a default XGBoost and explore the results
Task 5: Build a Deep Learning Model
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Select the "Read" button to begin. Build a default Deep Learning model and explore the results
Task 6: Tune the XGBoost Model with H2O GridSearch
Select the "Read" button to begin.
Select the "Read" button to begin. Tune your XGBoost model using H2O GridSearch and explore the results of the tuned model
Task 7: Tune the Deep Learning model with H2O GridSearch
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Select the "Read" button to begin. Tune your Deep Learning model using H2O GridSearch and explore the results of the tuned model
Task 8: Test Set Performance
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Select the "Read" button to begin. Check the test set performance of the best models
Task 9: Challenge
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Select the "Read" button to begin. Take on a challenge and see if you can build and tuned an extra H2O-3 model to get better results
Next Steps
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Select the "Read" button to begin.
Regression Tutorial Quiz - Python Version
25 Questions  |  2 attempts  |  20/25 points to pass
25 Questions  |  2 attempts  |  20/25 points to pass Take this quiz if you completed the Python version of the tutorial. If you completed the R version of the tutorial, you should take the quiz with the R version. Note that you only need to pass 1 quiz to obtain the badge.
Regression Tutorial Quiz - R Version
25 Questions  |  2 attempts  |  20/25 points to pass
25 Questions  |  2 attempts  |  20/25 points to pass Take this quiz if you completed the R version of the tutorial. If you completed the Python version of the tutorial, you should take the quiz with the Python version. Note that you only need to pass 1 quiz to obtain the badge.
Badge
  |  Badge available
  |  Badge available After passing the quiz, you can get your badge by clicking on "Badge Earned." Please check your email address for instructions on how to view, manage, and share your new badge!