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

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Objective
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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
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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.
Quiz
25 Questions  |  2 attempts  |  20/25 points to pass
25 Questions  |  2 attempts  |  20/25 points to pass
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