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Supervised learning involves training a model on labeled data from "summary" of Data Science for Business by Foster Provost,Tom Fawcett
Supervised learning is a type of machine learning where we have a specific outcome we want to predict or classify. In this setting, we first need to have labeled data, which means that we know the correct answer for each example in our dataset. This labeled data is used to train a model, which is essentially a function that maps input data to the correct output. The goal is for the model to learn how to make predictions or classifications on new, unseen data based on the patterns it has learned from the labeled data. The process of training a model on labeled data involves feeding the model examples from the dataset and adjusting its internal parameters to minimize the difference between its predictions and the true labels. This is typically done using an optimization algorithm that iteratively updates the model to improve its performance on the training data. The model is evaluated based on how well it can generalize to new, unseen data, which is crucial for its effectiveness in real-world applications. The labeled data used for training the model is essential for supervised learning because it provides the ground truth that the model needs to learn from. Without labeled data, the model would have no way of knowing whether its predictions are correct or not. The quality of the labeled data, including its accuracy and representativeness, plays a crucial role in the performance of the model. Therefore, it is important to carefully curate and preprocess the data before training the model to ensure that it learns meaningful patterns.- Supervised learning is a powerful approach in machine learning that relies on labeled data to train predictive models. By providing examples with known outcomes, we can teach the model to make accurate predictions on new data. This process of training a model on labeled data is fundamental to the success of supervised learning algorithms in a wide range of applications.
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