The hypothesis represents the learned or estimated relationship between input features and the target variable in supervised learning. For instance, in linear regression, the hypothesis might be a linear equation that predicts the target variable based on input features. In more complex models like neural networks, the hypothesis represents the network's learned mapping between inputs and outputs through its interconnected layers.

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The goal in supervised learning is to find the hypothesis that best fits the training data, allowing it to make accurate predictions on unseen or future data. This is achieved through the process of training the model using various algorithms and optimization techniques to minimize the difference between the predicted outputs (hypothesis) and the actual observed targets in the training data.