When exporting a model to ONNX format, which of the following algorithms can be effectively used in AutoML?

Exporting a model to ONNX (Open Neural Network Exchange) format means that the model is being converted into a standard format that can be used across various platforms and frameworks, thus enhancing its interoperability. Random forest algorithms are commonly used in AutoML because of their robustness, ability to handle large datasets, and no requirement for extensive parameter tuning. They are considered ensemble methods, which combine the predictions of multiple trees to improve accuracy and control over-fitting.

When it comes to ONNX, many libraries and frameworks support the random forest algorithm for export. The architecture of random forests, which are based on decision trees, allows them to be effectively translated into the ONNX format, ensuring that the model can be utilized in different environments without loss of functionality.

On the other hand, while linear regression and SVC (Support Vector Classifier) can also be exported to ONNX, they may not be ideal choices in every AutoML context due to their specific limitations or requirements for tuning. The Auto-ARIMA is more tailored for time series forecasting and does not lend itself as effectively to model interoperability with ONNX when compared to random forests. Thus, random forests stand out as a strong choice for exporting in the context of AutoML within the ONNX framework.