Yes, AutoML (Automated Machine Learning) can generate interpretable decision trees, but the interpretability depends on how the AutoML system is configured and the constraints applied during training. Decision trees are inherently explainable models because their structure—nodes representing feature-based decisions and leaves representing outcomes—can be visualized and traced logically. AutoML frameworks that support tree-based algorithms (like XGBoost, LightGBM, or CART) can produce decision trees, but the depth and complexity of those trees determine their interpretability. For example, a shallow tree with 3-4 levels is easier to understand than a deep, bushy tree with dozens of layers. Most AutoML tools allow users to set parameters like maximum tree depth or minimum samples per leaf to enforce simplicity, ensuring the output remains interpretable.
AutoML platforms like Google’s Vertex AI, H2O Driverless AI, or open-source libraries like TPOT and Auto-Sklearn include decision trees in their model search spaces. These tools automate hyperparameter tuning, feature preprocessing, and model selection, but they can prioritize simplicity if configured correctly. For instance, a developer using TPOT could limit the search to decision trees by restricting the model classifiers in the pipeline. Similarly, setting max_depth=3 in scikit-learn’s DecisionTreeClassifier (when integrated into an AutoML workflow) forces the tree to remain shallow, making it easier to visualize and explain. Some AutoML tools also provide post-training visualization options, such as exporting trees as text or graphical diagrams, which aid in interpreting decision rules.
However, there are trade-offs. AutoML systems often optimize for accuracy, which can lead to deeper trees or ensembles (e.g., random forests) that sacrifice interpretability for performance. For example, if an AutoML tool defaults to using gradient-boosted trees (which combine many weak trees), the resulting model becomes a “black box” despite individual trees being interpretable. To avoid this, developers must explicitly configure AutoML to prioritize interpretability—for instance, by disabling ensemble methods or enforcing strict complexity limits. In practice, this means balancing automation with manual oversight. A healthcare use case might involve an AutoML-generated decision tree with clear rules (e.g., “If age > 50 and blood pressure > 120, then high risk”) that clinicians can validate, whereas a marketing model might tolerate less interpretability for higher precision. The key takeaway: AutoML can produce interpretable decision trees, but it requires deliberate setup to ensure simplicity aligns with the project’s explainability needs.