Using a least-squares approach, we want to fit a regression model that consists of K linear...

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Using a least-squares approach, we want to fit a regression model that consists of K linear models M₁, M₂, ..., MK. That is, we want to partition the data points in the training set into K subsets T₁, T2, ...,Tk where each is used to build a separate linear model. Express the optimization problem associated with this approach in terms of wj, xi, yi, and lij. w; is an n-dimensional vector representing the weights of model M; x, is an n-dimensional vector representing the ith data point yi is a scalar representing the response associated with Xi - lij ( 1 if xi E Tj (0 otherwise Using a least-squares approach, we want to fit a regression model that consists of K linear models M₁, M₂, ..., MK. That is, we want to partition the data points in the training set into K subsets T₁, T2, ...,Tk where each is used to build a separate linear model. Express the optimization problem associated with this approach in terms of wj, xi, yi, and lij. w; is an n-dimensional vector representing the weights of model M; x, is an n-dimensional vector representing the ith data point yi is a scalar representing the response associated with Xi - lij ( 1 if xi E Tj (0 otherwise

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