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.

Read more in the User Guide.

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.

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_


Number of features seen during fit.

New in version 0.24.


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


Equivalent function without the estimator API.


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

For a comparison of the different scalers, transformers, and normalizers, see examples/preprocessing/plot_all_scaling.py.


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


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 output feature names for transformation.


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_output(*[, transform])

Set output container.


Set the parameters of this estimator.

transform(X[, y, copy])

Scale features of X according to feature_range.

__init__(feature_range=(0, 1), *, copy=True, clip=False)