class sklearn.preprocessing.MinMaxScaler(feature_range=(0, 1), copy=True)[source]

Standardizes 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 standardization 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 standardization is often used as an alternative to zero mean, unit variance scaling.


feature_range: tuple (min, max), default=(0, 1) :

Desired range of transformed data.

copy : boolean, optional, default True

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


min_ : ndarray, shape (n_features,)

Per feature adjustment for minimum.

scale_ : ndarray, shape (n_features,)

Per feature relative scaling of the data.


__init__(feature_range=(0, 1), copy=True)[source]
fit(X, y=None)[source]

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


X : array-like, shape [n_samples, n_features]

The data used to compute the per-feature minimum and maximum used for later scaling along the features axis.

fit_transform(X, y=None, **fit_params)[source]

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.


X : numpy array of shape [n_samples, n_features]

Training set.

y : numpy array of shape [n_samples]

Target values.


X_new : numpy array of shape [n_samples, n_features_new]

Transformed array.


Get parameters for this estimator.


deep: boolean, optional :

If True, will return the parameters for this estimator and contained subobjects that are estimators.


params : mapping of string to any

Parameter names mapped to their values.


Undo the scaling of X according to feature_range.


X : array-like with shape [n_samples, n_features]

Input data that will be transformed.


Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:self :

Scaling features of X according to feature_range.


X : array-like with shape [n_samples, n_features]

Input data that will be transformed.