vastorbit.machine_learning.vast.preprocessing.MinMaxScaler¶

class vastorbit.machine_learning.vast.preprocessing.MinMaxScaler(name: str = None, overwrite_model: bool = False)¶

i.e. Scaler with param method = ‘minmax’

Note

This is a child class. See Scaler for more details and examples.

Methods

`__init__`([name, overwrite_model])
`contour`([nbins, chart])	Draws the model's contour plot.
`deployInverseSQL`([key_columns, ...])	Returns the SQL code needed to deploy the inverse model.
`deploySQL`([X, key_columns, exclude_columns])	Returns the SQL code needed to deploy the model.
`drop`()	Drops the model from the VAST DataBase.
`export_models`(name, path[, kind])	Exports machine learning models.
`fit`(input_relation[, X, return_report])	Trains the model.
`get_attributes`([attr_name])	Returns the model attributes.
`get_match_index`(x, col_list[, str_check])	Returns the matching index.
`get_params`()	Returns the parameters of the model.
`get_plotting_lib`([class_name, chart, ...])	Returns the first available library (Plotly, Matplotlib) to draw a specific graphic.
`import_models`(path[, schema, kind])	Imports machine learning models.
`inverse_transform`(vdf[, X])	Applies the Inverse Model on a `VastFrame`.
`set_params`([parameters])	Sets the parameters of the model.
`summarize`()	Summarizes the model.
`to_binary`(path)	Exports the model to the VAST Binary format.
`to_memmodel`()	Converts the model to an InMemory object that can be used for different types of predictions.
`to_python`([return_proba, ...])	Returns the Python function needed for in-memory scoring without using built-in VAST functions.
`to_sql`([X, return_proba, ...])	Returns the SQL code needed to deploy the model without using built-in VAST functions.
`transform`([vdf, X])	Applies the model on a `VastFrame`.

Attributes