8.26.5. sklearn.preprocessing.MinMaxScaler

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

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.

Parameters :

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

Desired range of transformed data.

copy : boolean, optional, default is True

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

Attributes

min_ ndarray, shape (n_features,) Per feature adjustment for minimum.
scale_ ndarray, shape (n_features,) Per feature relative scaling of the data.

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_params([deep]) Get parameters for the estimator
inverse_transform(X) Undo the scaling of X according to feature_range.
set_params(**params) Set the parameters of the estimator.
transform(X) Scaling features of X according to feature_range.
__init__(feature_range=(0, 1), copy=True)
fit(X, y=None)

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

Parameters :

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)

Fit to data, then transform it

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

Parameters :

X : numpy array of shape [n_samples, n_features]

Training set.

y : numpy array of shape [n_samples]

Target values.

Returns :

X_new : numpy array of shape [n_samples, n_features_new]

Transformed array.

get_params(deep=True)

Get parameters for the estimator

Parameters :

deep: boolean, optional :

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

inverse_transform(X)

Undo the scaling of X according to feature_range.

Parameters :

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

Input data that will be transformed.

set_params(**params)

Set the parameters of the 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 :
transform(X)

Scaling features of X according to feature_range.

Parameters :

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

Input data that will be transformed.

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