Class MinMaxScaler (2.30.0)

MinMaxScaler()

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.

Methods

MinMaxScaler()

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.

__init__()

API documentation for __init__ method.

fit(X, y=None)

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

Returns
Type	Description
`MaxAbsScaler`	Fitted scaler.

fit_transform(X, y=None)

Fit to data, then transform it.

Parameters
Name	Description
`X`	`bigframes.dataframe.DataFrame or bigframes.series.Series` Series or DataFrame of shape (n_samples, n_features). Input samples.
`y`	`bigframes.dataframe.DataFrame or bigframes.series.Series` Series or DataFrame of shape (n_samples,) or (n_samples, n_outputs). Default None. Target values (None for unsupervised transformations).

Returns
Type	Description
`bigframes.dataframe.DataFrame`	DataFrame of shape (n_samples, n_features_new). Transformed DataFrame.

get_params(deep=True)

Get parameters for this estimator.

Parameter
Name	Description
`deep`	`bool, default True` Default `True`. If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns
Type	Description
`Dictionary`	A dictionary of parameter names mapped to their values.

to_gbq(model_name, replace=False)

Save the transformer as a BigQuery model.

Parameters
Name	Description
`model_name`	`str` The name of the model.
`replace`	`bool, default False` Determine whether to replace if the model already exists. Default to False.

transform(X)

Scale the data.

Returns
Type	Description
`bigframes.dataframe.DataFrame`	Transformed result.