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Version: v0.15.0

Metrics: Size dependency

H2O Model Validation offers an array of metrics in the form of a plot and heatmap to understand a size dependency test. Below, each metric is described in turn.

Plot graph: Modeling scores for different train dataset sizes (MSDTDS)​

The MSDTDS plot graph visualizes several scorer values for models (identical models) trained with different training data sizes. Observing the MSDTDS plot graph can enable you to understand and find an acceptable train dataset size for your model.

Plot details:

  • Y-Axis: The Y-Axis refers to scorer values
  • X-Axis: The X-Axis refers to the size of the training data
  • Yellow line: The yellow line refers to the validation data of the model
  • Red line: The red line refers to the test data of the model
  • Red or yellow dots: The red and yellow dots refer to a child model or the parent model.
    • Child model: A child model refers to a model trained on one of the sub-training samples obtained from the original training data
    • Parent model: The parent model refers to a trained model with the original training data and not with one of the sub-training samples.
      note

      To learn more about original training data and sub-training samples, see Size dependency.

Scores for models in the MSDTDS plot are positioned from left to right. The first model (also known as a child model) represents the model with the smallest sub-training sample obtained from the original train data. The model furthest to the right, known as the parent model, represents the original model that used the whole original train data. The model right before the parent model represents the model with the highest sub-training sample.

msdtds.png

example

Considering the below two graphs (figures) of a model and that a low RMSE score indicates a better model performance, we can conclude that as the model increases its training data size, the RMSE score will decrease while improving the model performance. When comparing the first child model with the parent model, we can see that by increasing the training data size by 144.0.66k, we will obtain a 0.48 RMSE score. A lower RMSE when compared to the one the first child model received.

child-plot.png

Figure 1: Child model

parent-plot.png

Figure 2: Parent model

Heatmap: Feature importance for different training data sizes (FIDTDS)​

The FIDTDS heatmap visualizes the most important features for different models trained on different sizes of train data during the retraining process of a model.

example

Based on the below heatmap, we can see that throughout the retraining process of a model with different sizes of train data, the customers variable was kept as the most important feature.

heatmap-feature-importance-for-different-training-data-size.png

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