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<a id="nets.opti.blackbox"></a>
# :orange[Hyper Paramaters Optimization class]
## nets.opti.blackbox
<a id="nets.opti.blackbox.Hyper"></a>
### 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.
<a id="nets.opti.blackbox.Hyper.start_study"></a>
#### 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
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