Unboxing H2O AutoML Models: A Comprehensive Guide

Introduction

Automated Machine Learning, or AutoML, is rapidly gaining popularity in the data science community. AutoML platforms aim to automate the time-consuming and iterative tasks inherent in developing machine learning models, including data preprocessing, feature engineering, model selection, hyperparameter tuning, and model evaluation. This promises to make machine learning more accessible and allow data scientists to develop high-performing models faster.

One of the leading AutoML platforms is H2O AutoML, developed by H2O.ai. H2O AutoML stands out for its high performance, wide range of supported algorithms, ability to handle structured and unstructured data, and suite of best-in-class explainability techniques. In this post, we‘ll dive deep into unboxing models developed with H2O AutoML, focusing on how to interpret the various explainability plots and outputs it provides.

Overview of H2O AutoML

At a high level, H2O AutoML trains and cross-validates a diverse set of algorithms on your dataset, including:

• Generalized linear models
• Gradient boosting machines (H2O‘s XGBoost)
• Distributed random forests
• Deep neural networks
• Stacked ensembles

It uses techniques like random grid search to tune hyperparameters and find optimal models for your data. The result is a leaderboard of high-performing models from different classes of algorithms. H2O AutoML can be used for regression, binary classification, and multi-class classification tasks on tabular data, as well as for image and text classification.

While H2O AutoML makes it easy to quickly develop accurate models, understanding what‘s happening inside the ‘black box‘ is crucial. H2O provides a best-in-class model explainability interface to help data scientists unbox and interpret their AutoML models. Let‘s walk through the various plots and outputs, using the Telco Customer Churn dataset as an example.

Variable Importance

The first key plot provided is the variable importance plot for your top model:

This plot shows the relative importance of the most influential variables in the model, scaled between 0 and 1. For tree-based models like gradient boosting machines and random forests, variable importance is calculated by the relative improvement each variable provides in squared error when chosen to split a node.

In this example, we can see that the customer‘s contract type is the most important predictor of churn, while whether they have multiple lines is the least important among the top variables. This gives quick insight into what factors have the greatest influence on your target variable.

Variable Importance Heatmap

While the variable importance plot shows the key drivers for a single model, the variable importance heatmap lets you compare variable importance across all the models on the leaderboard:

In this heatmap, models are on the x-axis and variables on the y-axis. Models are ordered by similarity, so models using the same algorithm family are grouped together. Darker red indicates higher importance for that variable and model combination.

Here we see that contract type is very important for the gradient boosting models but much less so for the deep learning models. Payment method shows the opposite pattern. This allows you to assess at a glance whether key relationships are consistent across model types.

Model Correlation Heatmap

Another useful plot is the model correlation heatmap:

This plot shows the correlation between model predictions, ordered by hierarchical clustering to group similar models. For classification, the metric is frequency of identical predictions.

The heatmap makes it easy to see that the GBM models are very similar to each other in their predictions but quite different from the deep learning models. The GLM model is more closely correlated with the deep learning models. Examining these relationships can help you avoid redundant models and select a diverse set for further analysis.

SHAP Summary Plot

SHAP (SHapley Additive exPlanations) values are a cutting-edge technique for interpreting model predictions. The SHAP summary plot shows, for each variable, the distribution of its impact on the model output:

Variables are ordered on the y-axis by importance, with more important variables at the top. The x-axis shows the SHAP value, interpreted as follows:

• Negative SHAP values (extending left from 0) indicate a variable value that reduces the model output
• Positive SHAP values (extending right from 0) indicate a variable value that increases the model output
• Color represents the actual variable value (pink=high, blue=low)

Each dot represents an individual observation. Dots stacked vertically correspond to all observations for that variable.

In our churn example, high monthly charges increase the likelihood of churn (pink dots extending far right), while having online security reduces it (blue dots extending left). The fact that monthly charges have points extending further on both sides indicates that variable has a bigger overall impact than online security.

Partial Dependence Plots

While SHAP values illustrate the effects of variables for individual observations, partial dependence plots (PDPs) show the marginal effect of a variable on the model output after accounting for the effects of all other variables.

The PDP plot shows the change in mean model output (y-axis) as the variable changes (x-axis), with a shaded confidence interval.

Here we see that longer tenure is associated with a lower probability of churn. By surfacing these univariate relationships, PDPs provide a useful complement to the multivariate relationships shown in SHAP plots.

Individual Conditional Expectation Plots

A limitation of PDPs is they only show the average relationship between a variable and the model output. Individual conditional expectation (ICE) plots extend PDPs by graphing the functional relationship for each individual observation:

In this example ICE plot, each line represents how the model output changes with age for an individual observation. The yellow line shows the average across all observations, matching the PDP.

The heterogeneous slopes of the lines indicates interactions between age and other variables in the model. While the model output generally decreases with age, this effect is less pronounced or even reversed for some observations. ICE plots are a powerful tool for detecting interactions and nonlinearities the model has learned.

Advantages and Limitations

H2O‘s explainability interface provides an extremely useful suite of plots and outputs for understanding your AutoML models. The visualizations are clear and the interpretations are intuitive, making it easy to extract insights even if you‘re not an expert in the underlying algorithms.

Crucially, the combination of global and local explanations via SHAP, PDP, and ICE gives a comprehensive view into the key drivers and complex relationships the model has uncovered. I especially appreciate that H2O automatically produces these plots, as generating them manually would be quite time-consuming.

One limitation is the lack of customization options for the visualizations. While effective for understanding the model, the plots are somewhat plain in appearance. Support for plot themes, custom labeling, and other visual tweaks would make them more engaging for presentations and reports. The interface would also benefit from displaying the performance metrics for the model being explained.

That said, these are minor drawbacks in the scheme of things. H2O deserves credit for incorporating cutting-edge explainability into its automated modeling process. After all, a highly accurate but opaque model is not as useful as an understandable one.

Conclusion

As we‘ve seen, H2O AutoML is a powerful platform for developing high-performing machine learning models quickly. For data scientists, however, simply producing accurate models is not enough – we must be able to explain how the models work and what they‘ve learned from the data.

H2O‘s explainability interface does a stellar job at enabling interpretation of AutoML models. By combining clear visualizations, multiple explanation modes, and detailed descriptions, it allows data scientists to unbox the black box and derive real insights from their models.

While there is always room for enhancement, H2O‘s leadership in model explainability sets a high bar for other AutoML platforms. I‘m excited to see how they continue to evolve these capabilities to make machine learning more transparent and accessible to all.