MinMaxScaler#

classsklearn.preprocessing.MinMaxScaler(feature_range=(0,1),*,copy=True,clip=False)[source]#

Transform features by scaling each feature to a given range.

This estimator scales and translates each feature individually suchthat it is in the given range on the training set, e.g. betweenzero 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 linearlyscales them down into a fixed range, where the largest occurring data pointcorresponds to the maximum value and the smallest one corresponds to theminimum value. For an example visualization, refer toCompareMinMaxScaler with other scalers.

Read more in theUser 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 acopy (if the input is already a numpy array).

clipbool, default=False

Set to True to clip transformed values of held-out data toprovidedfeaturerange.

Added in version 0.24.

Attributes:

min_ndarray of shape (n_features,)

Per feature adjustment for minimum. Equivalent tomin-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))

Added in version 0.17:scale_ attribute.

data_min_ndarray of shape (n_features,)

Per feature minimum seen in the data

Added in version 0.17:data_min_

data_max_ndarray of shape (n_features,)

Per feature maximum seen in the data

Added in version 0.17:data_max_

data_range_ndarray of shape (n_features,)

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

Added in version 0.17:data_range_

n_features_in_int

Number of features seen duringfit.

Added 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 acrosspartial_fit calls.

feature_names_in_ndarray of shape (n_features_in_,)

Names of features seen duringfit. Defined only whenXhas feature names that are all strings.

Added 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 intransform.

Examples

>>>fromsklearn.preprocessingimportMinMaxScaler>>>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. ]]

fit(X,y=None)[source]#

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

Parameters:

Xarray-like of shape (n_samples, n_features)

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

yNone

Ignored.

Returns:

selfobject

Fitted scaler.

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

Fit to data, then transform it.

Fits transformer toX andy with optional parametersfit_paramsand returns a transformed version ofX.

Parameters:

Xarray-like of shape (n_samples, n_features)

Input samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None

Target values (None for unsupervised transformations).

**fit_paramsdict

Additional fit parameters.

Returns:

X_newndarray array of shape (n_samples, n_features_new)

Transformed array.

get_feature_names_out(input_features=None)[source]#

Get output feature names for transformation.

Parameters:

input_featuresarray-like of str or None, default=None

Input features.

• Ifinput_features isNone, thenfeature_names_in_ isused as feature names in. Iffeature_names_in_ is not defined,then the following input feature names are generated:["x0","x1",...,"x(n_features_in_-1)"].

• Ifinput_features is an array-like, theninput_features mustmatchfeature_names_in_ iffeature_names_in_ is defined.

Returns:

feature_names_outndarray of str objects

Same as input features.

get_metadata_routing()[source]#

Get metadata routing of this object.

Please checkUser Guide on how the routingmechanism works.

Returns:

routingMetadataRequest

AMetadataRequest encapsulatingrouting information.

get_params(deep=True)[source]#

Get parameters for this estimator.

Parameters:

deepbool, default=True

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

Returns:

paramsdict

Parameter names mapped to their values.

inverse_transform(X)[source]#

Undo the scaling of X according to feature_range.

Parameters:

Xarray-like of shape (n_samples, n_features)

Input data that will be transformed. It cannot be sparse.

Returns:

X_originalndarray of shape (n_samples, n_features)

Transformed data.

partial_fit(X,y=None)[source]#

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

All of X is processed as a single batch. This is intended for caseswhenfit is not feasible due to very large number ofn_samples or because X is read from a continuous stream.

Parameters:

Xarray-like of shape (n_samples, n_features)

The data used to compute the mean and standard deviationused for later scaling along the features axis.

yNone

Ignored.

Returns:

selfobject

Fitted scaler.

set_output(*,transform=None)[source]#

Set output container.

SeeIntroducing the set_output APIfor an example on how to use the API.

Parameters:

transform{“default”, “pandas”, “polars”}, default=None

Configure output oftransform andfit_transform.

• "default": Default output format of a transformer

• "pandas": DataFrame output

• "polars": Polars output

• None: Transform configuration is unchanged

Added in version 1.4:"polars" option was added.

Returns:

selfestimator instance

Estimator instance.

set_params(**params)[source]#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects(such asPipeline). The latter haveparameters of the form<component>__<parameter> so that it’spossible to update each component of a nested object.

Parameters:

**paramsdict

Estimator parameters.

Returns:

selfestimator instance

Estimator instance.

transform(X)[source]#

Scale features of X according to feature_range.

Parameters:

Xarray-like of shape (n_samples, n_features)

Input data that will be transformed.

Returns:

Xtndarray of shape (n_samples, n_features)

Transformed data.

Gallery examples#

Time-related feature engineering

Time-related feature engineering

Image denoising using kernel PCA

Image denoising using kernel PCA

Selecting dimensionality reduction with Pipeline and GridSearchCV

Selecting dimensionality reduction with Pipeline and GridSearchCV

Univariate Feature Selection

Univariate Feature Selection

Recursive feature elimination

Recursive feature elimination

Scalable learning with polynomial kernel approximation

Scalable learning with polynomial kernel approximation

Manifold learning on handwritten digits: Locally Linear Embedding, Isomap…

Manifold learning on handwritten digits: Locally Linear Embedding, Isomap...

Evaluation of outlier detection estimators

Evaluation of outlier detection estimators

Compare Stochastic learning strategies for MLPClassifier

Compare Stochastic learning strategies for MLPClassifier

Compare the effect of different scalers on data with outliers

Compare the effect of different scalers on data with outliers

Release Highlights for scikit-learn 0.24

Release Highlights for scikit-learn 0.24