Feature-wise standardization for vector inputs. Notes ----- Due to the sensitivity of empirical mean and standard deviation calculations to extreme values, `Standardizer` cannot guarantee balanced feature scales in the presence of outliers. In partic
(self, with_mean=True, with_std=True)
| 140 | |
| 141 | class Standardizer: |
| 142 | def __init__(self, with_mean=True, with_std=True): |
| 143 | """ |
| 144 | Feature-wise standardization for vector inputs. |
| 145 | |
| 146 | Notes |
| 147 | ----- |
| 148 | Due to the sensitivity of empirical mean and standard deviation |
| 149 | calculations to extreme values, `Standardizer` cannot guarantee |
| 150 | balanced feature scales in the presence of outliers. In particular, |
| 151 | note that because outliers for each feature can have different |
| 152 | magnitudes, the spread of the transformed data on each feature can be |
| 153 | very different. |
| 154 | |
| 155 | Similar to sklearn, `Standardizer` uses a biased estimator for the |
| 156 | standard deviation: ``numpy.std(x, ddof=0)``. |
| 157 | |
| 158 | Parameters |
| 159 | ---------- |
| 160 | with_mean : bool |
| 161 | Whether to scale samples to have 0 mean during transformation. |
| 162 | Default is True. |
| 163 | with_std : bool |
| 164 | Whether to scale samples to have unit variance during |
| 165 | transformation. Default is True. |
| 166 | """ |
| 167 | self.with_mean = with_mean |
| 168 | self.with_std = with_std |
| 169 | self._is_fit = False |
| 170 | |
| 171 | @property |
| 172 | def hyperparameters(self): |
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