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\newcommand{\foldsize}{B} \newcommand{\nriter}{R} [/math]

Consider a ML application involving data points with features [math]\featurevec \in \mathbb{R}^{6}[/math] and a numeric label [math]\truelabel \in \mathbb{R}[/math]. We learn a hypothesis by minimizing the average loss incurred on a training set [math]\dataset = \big\{\big(\featurevec^{(1)},\truelabel^{(1)}\big),\ldots,\big(\featurevec^{(\samplesize)},\truelabel^{(\samplesize)}\big)\big\}[/math].

Which of the following ML methods uses a hypothesis space that depends on the dataset [math]\dataset[/math]?