# :orange[Hyper Paramaters Optimization class] ## nets.opti.blackbox ### Hyper Objects ```python class Hyper(SCI) ``` Hyper parameter tunning class. Allows to generate best NN architecture for task. Inputs are column indexes. idx[-1] is targeted value. #### start\_study ```python def start_study(n_trials: int = 100, neptune_project: str = None, neptune_api: str = None) ``` Starts study. Optionally provide your neptune repo and token for report generation. **Arguments**: - `n_trials` _int, optional_ - Number of iterations. Defaults to 100. - `neptune_project` _str, optional_ - None - neptune_api (str, optional):. Defaults to None. **Returns**: - `dict` - quick report of results