The correct option is scikit-learn.

scikit-learn is designed for traditional supervised learning on tabular data and it provides a broad set of well tested classical classification algorithms such as logistic regression, decision trees, random forests, and gradient boosting that are easy to train and evaluate. It has a simple API and is ideal for quick prototyping and productionizing conventional classification models in a cloud machine learning workspace.

PyTorch is a deep learning framework that is excellent for building custom neural networks and research oriented models but it is generally more complex and resource intensive than needed for a standard tabular classification task.

TensorFlow is also focused on deep learning and scalable model training and serving. It can solve classification problems but it is usually a heavier choice compared with libraries that specialize in classical machine learning for tabular data.

ONNX is an open model format and runtime for interoperability and deployment rather than a primary training framework. It is useful for exporting or running models but it is not the best answer when choosing a framework to build a conventional classifier from scratch.

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