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In this section we are concerned with the our model hyperparameters.  ## Kernel Approximation Hyperparameters  - All features:  - **k**: number of components/dimension of feature-space.  - Sparse features:   - **r**: value below which a kernel value will be set to zero.  ## SGD Hyperparameters  - \(\boldsymbol{\alpha}\): the descent rate of our gradient method.