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 this estimator. inverse_transform(X) Undo the scaling of X according to feature_range. set_params(**params) Set the parameters of this 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 this estimator.
Parameters : deep: boolean, optional :
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns : params : mapping of string to any
Parameter names mapped to their values.
- 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 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 :
- 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.