dask_ml.preprocessing.MinMaxScaler

dask_ml.preprocessing.MinMaxScaler

class dask_ml.preprocessing.MinMaxScaler(feature_range=(0, 1), *, copy=True, clip=False)

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, e.g. between zero and one.

The transformation is given by:

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.

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

MinMaxScaler doesn’t reduce the effect of outliers, but it linearly scales them down into a fixed range, where the largest occurring data point corresponds to the maximum value and the smallest one corresponds to the minimum value. For an example visualization, refer to Compare MinMaxScaler with other scalers.

Read more in the User Guide.

Parameters
feature_rangetuple (min, max), default=(0, 1)

Desired range of transformed data.

copybool, default=True

Set to False to perform inplace row normalization and avoid a copy (if the input is already a numpy array).

clipbool, default=False

Set to True to clip transformed values of held-out data to provided feature range.

New in version 0.24.

Attributes
min_ndarray of shape (n_features,)

Per feature adjustment for minimum. Equivalent to min - X.min(axis=0) * self.scale_

scale_ndarray of shape (n_features,)

Per feature relative scaling of the data. Equivalent to (max - min) / (X.max(axis=0) - X.min(axis=0))

New in version 0.17: scale_ attribute.

data_min_ndarray of shape (n_features,)

Per feature minimum seen in the data

New in version 0.17: data_min_

data_max_ndarray of shape (n_features,)

Per feature maximum seen in the data

New in version 0.17: data_max_

data_range_ndarray of shape (n_features,)

Per feature range (data_max_ - data_min_) seen in the data

New in version 0.17: data_range_

n_features_in_int

Number of features seen during fit.

New in version 0.24.

n_samples_seen_int

The number of samples processed by the estimator. It will be reset on new calls to fit, but increments across partial_fit calls.

feature_names_in_ndarray of shape (n_features_in_,)

Names of features seen during fit. Defined only when X has feature names that are all strings.

New in version 1.0.

See also

minmax_scale

Equivalent function without the estimator API.

Notes

NaNs are treated as missing values: disregarded in fit, and maintained in transform.

Examples

>>> from sklearn.preprocessing import MinMaxScaler
>>> data = [[-1, 2], [-0.5, 6], [0, 10], [1, 18]]
>>> scaler = MinMaxScaler()
>>> print(scaler.fit(data))
MinMaxScaler()
>>> print(scaler.data_max_)
[ 1. 18.]
>>> print(scaler.transform(data))
[[0.   0.  ]
 [0.25 0.25]
 [0.5  0.5 ]
 [1.   1.  ]]
>>> print(scaler.transform([[2, 2]]))
[[1.5 0. ]]

Methods

fit(X[, y])

Compute the minimum and maximum to be used for later scaling.

fit_transform(X[, y])

Fit to data, then transform it.

get_feature_names_out([input_features])

Get output feature names for transformation.

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

inverse_transform(X[, y, copy])

Undo the scaling of X according to feature_range.

partial_fit(X[, y])

Online computation of min and max on X for later scaling.

set_inverse_transform_request(*[, copy])

Request metadata passed to the inverse_transform method.

set_output(*[, transform])

Set output container.

set_params(**params)

Set the parameters of this estimator.

set_transform_request(*[, copy])

Request metadata passed to the transform method.

transform(X[, y, copy])

Scale features of X according to feature_range.
__init__(feature_range=(0, 1), *, copy=True, clip=False)

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