Cattleia: A Tool for Analyzing Ensemble Models in AutoML

In many cases, combining different predictive models, a process called model ensembling, leads to better outcomes than using a single model. This technique is often used in Automated Machine Learning (AutoML). However, the most popular AutoML frameworks tend to create Ensembles that are difficult to understand. This paper introduces cattleia, an application designed to clarify ensembles for tasks such as regression, multiclass, and binary classification. Cattleia works with models created using three AutoML packages: auto-sklearn, AutoGluon, and FLAML.

Cattleia analyzes the given ensemble from multiple angles. It investigates how well the ensemble performs by looking at various evaluation Metrics related to both the ensemble and its individual models. Additionally, it introduces new measures to evaluate how diverse and complementary the models are in their predictions. To understand how important different variables are, the tool uses explainable artificial intelligence (XAI) techniques. Summarizing these insights, users can adjust the weights of the models in the ensemble to optimize its performance. The application features interactive visualizations, making it user-friendly for a wide audience.

We believe that cattleia can aid users in making informed decisions and deepen their knowledge of AutoML frameworks. In many machine learning tasks, the goal is to develop accurate, reliable, and general models. Ensembles of predictive models have proven to be particularly effective in achieving these goals. Consequently, they are commonly included in AutoML packages that aim to produce the best possible models.

The effectiveness of an ensemble largely depends on the diversity of models included in it. By selecting models that provide different predictions, the ensemble can achieve greater flexibility and generalization. Ideally, different algorithms with varying hyperparameters should be included to foster this diversity. There are various ways to create diverse models, such as iterative approaches or pruning methods, along with basic techniques such as boosting, bagging, and stacking.

While ensemble methods are powerful, questions remain about whether it is possible to improve results without sacrificing model understandability. It is important to grasp the significance of model diversity and how models relate to one another. Increasing interest in explainable machine learning suggests that there is a need to support the decision-making process, enhance trust in AutoML models, and utilize them effectively.

Most tools and visualizations available today focus on post-modeling processes, with less attention paid to the classification of ensemble models. This paper presents cattleia, which stands for Complex Accessible Transparent Tool for Learning Ensembles in AutoML. Cattleia aims to close these gaps and contribute to the understanding of AutoML explanations. The tool is developed using the Dash web framework and enhances model interpretability by offering new solutions for analyzing ensembles.

Cattleia is compatible with three prominent AutoML packages: AutoGluon, auto-sklearn, and FLAML. The application provides analysis from four distinct perspectives: metrics that evaluate individual models and the ensemble itself, compatimetrics that assess the relationships between models, weights assigned to specific models in the ensemble, and XAI methods that evaluate the importance of variables.

The analysis can look at the entire ensemble or focus on pairs of models, individual models, specific variables, and particular observations. This tool supports data scientists in interacting with established AutoML frameworks while providing visualizations and metrics that ease the learning curve for exploring AutoML solutions.

#Related Tools

Existing AutoML frameworks display model performance in various ways, making comparisons difficult and requiring improvement. Several tools have been developed to address this issue, primarily focusing on the model creation process in AutoML frameworks.

One such tool is ATMSeer, which helps monitor an ongoing AutoML process. It allows users to analyze the models being searched and refine the search space in real-time through visualizations.

Another interactive visualization tool is PipelineProfiler, which is integrated with Jupyter Notebook. It helps users explore and compare machine learning pipelines generated by different AutoML systems, presenting the information in a matrix format that summarizes structure and performance.

XAutoML is also an interactive visual analytics tool that addresses the needs of a diverse user group. It allows users to compare pipelines, analyze the optimization process, inspect individual models, and evaluate ensembles. This tool integrates with JupyterLab for a streamlined experience and includes a hyperparameter importance visualization.

AutoAIViz is a system aimed at visualizing the model generation process in AutoML. It provides real-time overviews of the pipelines and detailed information at each step of the process.

DeepCAVE is an interactive framework for analyzing and monitoring AutoML optimization. It offers an app for real-time visualization and analysis across various domains, including performance analysis and hyperparameter evaluation.

Though many studies have been published regarding explanations of AutoML models, most have focused on the model-building phase. More tools are needed for a comprehensive evaluation of the outcomes of built models and performance comparisons of the models used in ensembles.

Cattleia is introduced as an application that analyzes model ensembles created by popular AutoML packages in Python. It is available on GitHub as an open-source project.

Cattleia generates visualizations using the Plotly library, which allows for interactive features like zooming and filtering. The application performs analyses on pre-trained models without the need to train them from scratch, ensuring smooth performance. One of its key features is customizability, allowing users to add new metrics and packages as needed.

#Application Interface

The cattleia application interface is organized into four tabs related to different aspects of ensemble analysis. The left sidebar includes instructions and a section to upload the ensemble being examined.

The application also includes a user-selectable instructional guide, which explains how to use the tool effectively. Users must supply both the data and the model created with the supported AutoML packages, saving it in a specified format. The annotations feature can display descriptions helpful for interpreting visualizations. Once the necessary elements are uploaded, the user is presented with an interactive dashboard.

The available tabs represent various scopes of ensemble analysis:

#Metrics Tab

The metrics tab includes a comparison of evaluation metrics for both the component models and the ensemble. Depending on whether the model addresses a classification or regression problem, corresponding metrics and graphs are displayed. Additionally, this tab includes a correlation matrix of each model’s predictions, along with a plot comparing individual predictions with the actual target values.

#Compatimetrics Tab

The compatimetrics tab evaluates the similarity and joint performance of models in the ensemble. It introduces new measures of model compatibility based on simple heuristics and evaluation metrics, allowing a deeper analysis to uncover hidden patterns among models and identify groups that work well together.

#Weights Analysis Tab

This tab examines how much each component model contributes to the overall score of the ensemble. Designed specifically for AutoGluon and auto-sklearn, it uses interactive sliders that let users adjust the influence of specific models in the predictions. This feature enables users to see how metrics change with various custom weights.

#XAI Tab

The XAI tab assesses the significance of variables in individual models. The methods used are model-agnostic, meaning they can be applied across various models. Plots depict how important different features are and show how changes in variable values affect predictions.

#Use Cases

Cattleia is a valuable tool for data scientists in their daily tasks. There is a clear demand for tools that explain model ensembles. Cattleia only requires data and a pre-trained ensemble, providing a comprehensive dashboard for users. The following sections describe different situations users may encounter and propose solutions within the cattleia app, along with real-life examples of analyses obtained from the application.

#Evaluation of Component Models

#Problem

Ensembles often consist of models with varying performance levels. Including less effective models may help capture certain prediction patterns from complex data samples. It’s vital to compare models' performance on both training and testing sets to ensure they can generalize effectively to unseen data.

#Solution

Users can easily examine the performance of each component model and the ensemble as a whole through the evaluation metrics tab. This tab provides classification and regression measures that allow for a thorough analysis of each model's quality. The prediction compare matrix helps identify which models struggle with specific data points but may still excel in particular areas.

#Examining Model Diversity

#Problem

Creating strong ensembles necessitates including models that provide varied predictions. This aspect is essential since diverse models can leverage their individual strengths on specific data observations. Evaluating model similarity requires special measures to compare them effectively.

#Solution

The compatimetrics tab analyzes how similar model predictions are, providing insights into their compatibility. By using diverse measures, users can identify model groups that work well together or those that may harm overall predictions.

#Addressing Sensitive Data

#Problem

Fairness is critical in many machine learning applications. Algorithms must not discriminate against certain groups. Understanding how models behave before deploying them is essential to avoid potential issues.

#Solution

Using XAI techniques, cattleia lets users assess how important specific variables are for individual models. By analyzing feature importance and partial dependence plots, users can determine how each variable impacts model predictions, allowing for adjustments to mitigate unfair behavior.

#Adjusting Weights

#Problem

Assigning weights to model predictions is crucial for deriving the final output of an ensemble. Weights determine how much influence each model has on the overall performance, making weight distribution analysis essential.

#Solution

The weight modification tool enables users to explore and adjust the weight allocation among the models in an ensemble. It allows for testing the impact of such adjustments on performance without needing to retrain the models.

#Summary of Use Cases

The analysis of real-life use cases demonstrates that cattleia can enhance users' understanding of ensemble models. The application allows for a closer examination of how ensembles are constructed, the performance of individual models, and the influence of various factors on final predictions.

Cattleia discourages reliance on models without a clear understanding of their workings. This tool offers an in-depth look at ensembles trained using AutoML packages, providing a clear rationale for planning in real-world scenarios, which is essential when using artificial intelligence.

Despite its many features, cattleia is not without limitations. One major limitation is the number of frameworks currently supported. Cattleia works with three popular frameworks, but future plans include maintenance and support for new AutoML packages. Enhanced analysis, additional visualization options, and the expansion of compatimetrics definitions are other goals for future development.

#Broader Impact

Cattleia is a versatile tool that can be applied in various areas where supervised machine learning models are utilized. Its main goal is to clarify ensemble models created by AutoML frameworks, helping users make sense of individual decisions and the models behind them.

The tool can improve transparency in critical applications, such as medicine and finance, by examining ensembles and their base models. This examination can help address fairness issues and identify unwanted relationships in committees, leading to more trustworthy models.

At the same time, it’s important to remember that using a dashboard like cattleia without sufficient domain knowledge can have negative consequences. Misunderstanding certain methods may lead to incorrect assumptions about ensembles and their outputs. However, by providing clear annotations linked to visualizations, cattleia enables users to engage with the results meaningfully and accurately.

#Conclusion

Cattleia stands as a vital resource for users seeking to understand the intricacies of ensemble models in AutoML. Its user-friendly interface, coupled with a diverse range of analyses, empowers data scientists to make informed, data-driven decisions. As the field of AutoML continues to grow, tools like cattleia will be essential in addressing the increasing demand for model interpretability, transparency, and reliability in machine learning.