AutoML tools can provide some level of explanation for their results, but the depth and clarity of these explanations can vary significantly depending on the specific tool and the underlying model used. Many AutoML frameworks are designed to automate the machine learning process, which includes tasks like model selection, hyperparameter tuning, and preprocessing. They often focus on optimizing model performance rather than offering a comprehensive understanding of how the data was processed or how predictions were made. However, certain AutoML tools do incorporate explainability features to help users understand their models better.
For example, some AutoML solutions integrate techniques like SHAP (SHapley Additive exPlanations) or LIME (Local Interpretable Model-agnostic Explanations). These techniques help to interpret how different features contribute to model predictions. If a developer uses an AutoML tool that supports these techniques, they can obtain insights into which features were most influential for particular decisions made by the model. This can be particularly useful in industries where understanding the rationale behind a prediction is critical, such as in finance or healthcare.
That said, the effectiveness of explanations can depend on the complexity of the model. For instance, a decision tree might offer clearer insights compared to a deep learning model, which tends to be more of a "black box." Developers using AutoML should verify whether their chosen tool provides sufficient explanation capabilities and whether it aligns with their project requirements. If explainability is crucial for the application, they might need to explore additional methods or tools dedicated to model interpretation to supplement the AutoML results.