A hyperplane H of a near-linear space S is a proper subspace of... | Filo
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In three dimensions, linear regression can be visualized as a plane that best fits the data. When we move into higher dimensions, this idea extends to an n-dimensional hyperplane that represents the model’s predictions. Training this model relies on calculus and linear algebra, along with a way to measure how well the hyperplane matches the data. This measurement is the loss function, most commonly the mean squared error (MSE) or similar variants. During training, we pass input data through the
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Insightforge | AI & Data Science on Instagram: "In three dimensions, linear regression can be visualized as fitting a plane to the data. When extended to higher dimensions, the model fits an n-dimensional hyperplane that best represents the relationship between the features and the target variable. Training this model relies on calculus and linear algebra, along with a way to measure how well the plane fits the data known as the loss function. Common choices include Mean Squared Error (MSE) and
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