Estimate the optimal parameter lambda for each feature. The optimal lambda parameter for minimizing skewness is estimated on each feature independently using maximum likelihood. Parameters ---------- X : array-like of shape (n_samples, n_features)
(self, X, y=None)
| 3365 | |
| 3366 | @_fit_context(prefer_skip_nested_validation=True) |
| 3367 | def fit(self, X, y=None): |
| 3368 | """Estimate the optimal parameter lambda for each feature. |
| 3369 | |
| 3370 | The optimal lambda parameter for minimizing skewness is estimated on |
| 3371 | each feature independently using maximum likelihood. |
| 3372 | |
| 3373 | Parameters |
| 3374 | ---------- |
| 3375 | X : array-like of shape (n_samples, n_features) |
| 3376 | The data used to estimate the optimal transformation parameters. |
| 3377 | |
| 3378 | y : None |
| 3379 | Ignored. |
| 3380 | |
| 3381 | Returns |
| 3382 | ------- |
| 3383 | self : object |
| 3384 | Fitted transformer. |
| 3385 | """ |
| 3386 | self._fit(X, y=y, force_transform=False) |
| 3387 | return self |
| 3388 | |
| 3389 | @_fit_context(prefer_skip_nested_validation=True) |
| 3390 | def fit_transform(self, X, y=None): |