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 advantages of supervised learning is that it allows you to train a model to perform specific tasks by providing it with labeled examples of the desired behavior. This makes supervised learning particularly well-suited for tasks such as classification, regression, and anomaly detection, where the goal is to predict a specific output based on input data. By using labeled data to train models, supervised learning enables you to leverage the knowledge encapsulated in the training examples to make accurate predictions on new, unseen data. This helps you build models that can generalize well to new situations and make informed decisions based on the patterns and relationships present in the data.- Supervised learning provides a powerful framework for training models that can make accurate predictions by learning from labeled examples. By leveraging the information contained in labeled data, you can build models that can perform a wide range of tasks, from classifying images to predicting stock prices, with a high degree of accuracy and reliability.
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