# minmax_scale

### cuml.preprocessing.minmax_scale(X, feature_range=(0, 1), , axis=0, copy=True)[[source]](https://github.com/NVIDIA/cuml/blob/93418baf5/python/cuml/cuml/_thirdparty/sklearn/preprocessing/_data.py#L461)

Transform features by scaling each feature to a given range.

This estimator scales and translates each feature individually such
that it is in the given range on the training set, i.e. between
zero and one.

The transformation is given by (when `axis=0`):

```default
X_std = (X - X.min(axis=0)) / (X.max(axis=0) - X.min(axis=0))
X_scaled = X_std * (max - min) + min
```

where min, max = feature_range.

The transformation is calculated as (when `axis=0`):

```default
X_scaled = scale * X + min - X.min(axis=0) * scale
where scale = (max - min) / (X.max(axis=0) - X.min(axis=0))
```

This transformation is often used as an alternative to zero mean,
unit variance scaling.

* **Parameters:**
  **X**
  : The data.

  **feature_range**
  : Desired range of transformed data.

  **axis**
  : Axis used to scale along. If 0, independently scale each feature,
    otherwise (if 1) scale each sample.

  **copy**
  : Whether a forced copy will be triggered. If copy=False, a copy might
    be triggered by a conversion.

#### SEE ALSO
[`MinMaxScaler`](../cuml.preprocessing.MinMaxScaler/index.html.md#cuml.preprocessing.MinMaxScaler)
: Performs scaling to a given range using the\`\`Transformer\`\` API

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