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Okay, let's get a bit more technical. Don't worry; we'll keep it as simple as possible. The primary goal of Lasso Regression, like all regression models, is to find the best-fitting line (or hyperplane) that represents the relationship between your predictor variables and the target variable. The robert hays today "best-fitting" line is determined by minimizing a loss function. The loss function measures how well the model's predictions match the actual values. In the case of Lasso Regression, the loss function is the sum of two components: the residual sum of squares (RSS) and the penalty term.
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