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GBM Tutorial

The purpose of this tutorial is to walk new users through a GBM analysis in H2O Flow.

Those who have never used H2O before should refer to Getting Started for additional instructions on how to run H2O Flow.

Getting Started

This tutorial uses a publicly available data set that can be found at: <ahref="http://archive.ics.uci.edu/ml/datasets/Arrhythmia" target="_blank">http://archive.ics.uci.edu/ml/datasets/Arrhythmia.

The original data are the Arrhythmia data set made available by UCI Machine Learning repository. They are composed of 452 observations and 279 attributes.

If you don't have any data of your own to work with, you can find some example datasets here: http://data.h2o.ai

Importing Data

Before creating a model, import data into H2O:

  1. Click the Assist Me! button (the last button in the row of buttons below the menus).

Assist Me button 2. Click the importFiles link and enter the file path to the dataset in the Search entry field. 3. Click the Add all link to add the file to the import queue, then click the Import button. Importing Files

Parsing Data

Now, parse the imported data:

  1. Click the Parse these files... button.

Note: The default options typically do not need to be changed unless the data does not parse correctly.

  1. From the drop-down Parser list, select the file type of the data set (Auto, XLS, CSV, or SVMLight).
  2. If the data uses a separator, select it from the drop-down Separator list.
  3. If the data uses a column header as the first row, select the First row contains column names radio button. If the first row contains data, select the First row contains data radio button. You can also select the Auto radio button to have H2O automatically determine if the first row of the dataset contains the column names or data.
  4. If the data uses apostrophes ( ' - also known as single quotes), check the Enable single quotes as a field quotation character checkbox.
  5. Review the data in the Data Preview section, then click the Parse button.

Parsing Data

NOTE: Make sure the parse is complete by confirming progress is 100% before continuing to the next step, model building. For small datasets, this should only take a few seconds, but larger datasets take longer to parse.

Building a Model

  1. Once data are parsed, click the View button, then click the Build Model button.
  2. Select Gradient Boosting Machine from the drop-down Select an algorithm menu, then click the Build model button.
  3. If the parsed arrhythmia.hex file is not already listed in the Training_frame drop-down list, select it. Otherwise, continue to the next step.
  4. From the Ignored_columns section, select the columns to ignore in the Available area to move them to the Selected area. For this example, do not select any columns.
  5. From the drop-down Response list, select column 1 (C1).
  6. In the Ntrees field, specify the number of trees to build (for this example, 20).
  7. In the Max_depth field, specify the maximum number of edges between the top node and the furthest node as a stopping criteria (for this example, use the default value of 5).
  8. In the Min_rows field, specify the minimum number of observations (rows) to include in any terminal node as a stopping criteria (for this example, 25).
  9. In the Nbins field, specify the number of bins to use for data splitting (for this example, use the default value of 20). The split points are evaluated at the boundaries at each of these bins. As the value of Nbins increases, the algorithm approximates more closely the evaluation of each individual observation as a split point. The cost of this refinement is an increase in computational time.
  10. In the Learn_rate field, specify the tuning parameter (also known as shrinkage) to slow the convergence of the algorithm to a solution, which helps prevent overfitting. For this example, enter 0.3.
  11. Click the Build Model button.

Building Models

Viewing GBM Results

The output for GBM includes the following:

  • Model parameters (hidden)

  • A graph of the scoring history (training MSE vs number of trees)

  • A graph of the variable importances

  • Output (model category, validation metrics, initf)

  • Model summary (number of trees, min. depth, max. depth, mean depth, min. leaves, max. leaves, mean leaves)

  • Scoring history in tabular format

  • Training metrics (model name, model checksum name, frame name, description, model category, duration in ms, scoring time, predictions, MSE, R2)

  • Variable importances in tabular format

  • POJO Preview

    GBM Model Results

For classification models, the MSE is based on the classification error within the tree. For regression models, MSE is calculated from the squared deviances, as it is in standard regressions.

Viewing Predictions

To view predictions, click the Predict button. From the drop-down Frame list, select the arrhythmia.hex file and click the Predict button.

To view more prediction data, click the View Prediction Frame button.

GBM: Viewing Prediction Frame