=================== Parameter Selection =================== Most baseline algorithms in pybaselines have several parameters that can be adjusted. While this allows for fine-tuning each algorithm to work in a wide array of cases, it can also present a difficulty for new users. It is suggested to start by adjusting only one or two main parameters, and then change other parameters as needed. **Due to the variable nature of baselines, it is highly recommended to not assume the default parameters will work for your data!** Below are the suggested parameters to begin adjusting for each family of algorithms within pybaselines: * Polynomial methods * ``poly_order`` controls the curvature of the baseline. * Whittaker-smoothing-based methods * ``lam`` controls the curvature of the baseline. See :ref:`this example ` to get an idea of how ``lam`` effects the baseline. The optimal ``lam`` value for each algorithm is not typically the same. * Morphological methods * ``half_window`` controls the general fit of the baseline. See :ref:`this example ` to get an idea of how ``half_window`` effects the baseline. The optimal ``half_window`` value for each algorithm is not typically the same. * Spline methods * ``lam`` controls the curvature of the baseline. The :ref:`Whittaker example ` also generally applies to spline methods. * Smoothing-based methods * ``half_window`` controls the general fit of the baseline. The :ref:`Morphological example ` also generally applies to smoothing methods. * Baseline/Peak Classification methods * Algorithm dependent * Optimizers * Algorithm dependent * Miscellaneous methods * Algorithm dependent