GBM Tutorial

This tutorial walks the new user through a GBM analysis in H2O.

If you have never used H2O before, refer to the quick start guide for additional instructions on how to run H2O Getting Started From a Downloaded Zip File.

Getting Started

This tutorial uses a publicly available data set that can be found at: 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.

Before modeling, parse data into H2O:

  1. From the drop-down Data menu, select Upload and use the uploader to upload data.
  2. On the “Request Parse” page that appears, check the “header” checkbox if the first row of the data set is a header. No other changes are required.
  3. Click Submit. Parsing data into H2O generates a .hex key in the format “data name.hex”
../_images/PCAparse.png

Building a Model

  1. Once data are parsed, a horizontal menu displays at the top of the screen reading “Build model using ... ”. Select GBM here, or go to the drop down menu Model and select GBM.
  2. In the “source” field, enter the .hex key for the Arrhythmia data set.
  3. In the “response” list, select the response variable. In this example, it is variable 1.
  4. In the “Ignored Columns” section, select the subset of variables to omit from the model. In this example, the only column to omit is the index column, 0.
  5. Select Gradient Boosted Classification by checking the “classification” checkbox or Gradient Boosted Regression by unchecking the “classification” checkbox. GBM is set to classification by default. For this example, the desired output is classification.
  6. In the “validation” field, enter the .hex key associated with a holdout (testing) data set to apply results to a new data set after the model is generated.
  7. In the “ntrees” field, enter the number of trees to generate. For this example, enter 20.
  8. 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, set the depth of interaction to 5.
  9. 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, use 25.
  10. In the “nbins” field, specify the number of bins to use for splitting data. Split points are evaluated at the boundaries of each of these bins. As the value for Nbins increases, the more closely the algorithm approximates evaluating each individual observation as a split point. The trade off for this refinement is an increase in computational time.
  11. In the “learn rate” field, specify a value to slow the convergence of the algorithm to a solution and help prevent overfitting. This parameter is also referred to as shrinkage. In this example, enter .3.
../_images/GBMrequest.png

GBM Results

The GBM output for classification displays a confusion matrix with the classifications for each group, the associated error by group, and the overall average error. Regression models can be quite complex and difficult to directly interpret. For that reason, a model key is given for subsequent use in validation and prediction.

Both model types provide the MSE by tree. 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 with standard regressions.

../_images/GBMresults.png