Azure Data Scientists Associate Practice Exam

Which algorithm can be exported to ONNX models but is mainly used for classification?

1. Random forest

2. Auto-ARIMA

3. SVC algorithm

4. Decision tree

The correct answer is: SVC algorithm

The support vector classification (SVC) algorithm is primarily designed for classification tasks, making it a suitable choice for this question. SVC works by finding the hyperplane that best separates the classes in a high-dimensional feature space. It can effectively handle both linear and non-linear classification problems by using different kernel functions. One of the key advantages of SVC is its ability to be exported as an ONNX (Open Neural Network Exchange) model. ONNX is a standard format for representing machine learning models that allows interoperability between different frameworks. This capability is particularly useful in environments where you want to deploy models across various hardware and platforms without being tied to a specific library or framework. In contrast, while other algorithms listed can also be used for classification, they do not share the same level of versatility in terms of model exportability to ONNX. Random forests and decision trees, being ensemble and single tree methods respectively, do have ONNX support, but they are not as commonly associated with this functionality. Auto-ARIMA is specifically tailored for time series forecasting and is not applicable to classification tasks. Therefore, SVC stands out as the correct choice due to its specific focus on classification and its compatibility with the ONNX format.