The fundamental sampling method used in supervised learning is a labeled dataset. As described in Juniper Mist AI and machine learning application guides, supervised learning algorithms are trained using datasets in which each sample is paired with a known output (label). The model is fed examples comprising input data and the correct answer, learning patterns to map new inputs to their target outputs. While regression, decision tree, and random forest are techniques within supervised learning, the process fundamentally relies on having a labeled dataset for both training and validation phases. “Supervised machine learning models are built on labeled data, with each input paired to a target value or classification.” This allows for accurate model training and evaluation.
[Reference:Juniper Mist AI Explainable AI Reference , Machine Learning in Juniper Mist, , , ]
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