Yes, AutoML systems can support custom metrics, though the extent of this support depends on the specific platform or framework being used. AutoML tools are designed to automate tasks like model selection, hyperparameter tuning, and feature engineering, but many also allow developers to define custom evaluation metrics tailored to their project’s needs. This flexibility is critical because real-world problems often require metrics beyond standard ones like accuracy or F1-score. For example, a medical diagnosis model might prioritize minimizing false negatives over other errors, necessitating a custom metric that penalizes missed diagnoses more heavily.
Platforms like Google’s AutoML Tables, H2O Driverless AI, and open-source libraries like Auto-sklearn or TPOT provide mechanisms to integrate custom metrics. In Google’s AutoML, for instance, you can define a custom metric by writing a Python function that calculates the metric based on predicted and actual values, then pass it to the training process. Similarly, H2O’s Driverless AI allows users to specify custom scoring functions during experiment setup. These tools typically require the metric to adhere to a specific format, such as a function that takes true labels and predictions as inputs and returns a scalar value. This ensures compatibility with the AutoML system’s optimization pipeline, which uses the metric to compare and select the best models.
However, there are limitations and considerations. First, not all AutoML platforms support custom metrics—some restrict users to predefined options. Second, custom metrics must align with the tool’s underlying optimization process. For example, if an AutoML system uses gradient-based optimization, the metric may need to be differentiable, which isn’t always feasible for highly specialized metrics. Additionally, developers must ensure the custom metric is computationally efficient, as complex calculations could slow down the automated training process. Despite these caveats, the ability to define custom metrics in AutoML enables teams to align model evaluation with business goals, making it a valuable feature for domain-specific applications.