Supervised learning uses labeled data for training models from "summary" of Data Science For Dummies by Lillian Pierson
In supervised learning, you typically start with a dataset that contains examples of input data points along with their corresponding output labels. These output labels act as the "answers" that you want your model to be able to predict. By training a model on this labeled data, you're essentially teaching it how to map input data to the correct output labels. During the training process, the model learns from the labeled examples in the dataset by adjusting its internal parameters to minimize the error between its predictions and the actual output labels. This iterative process continues until the model has learned to make accurate predictions on new, unseen data. One of the key adva...
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