Class: RobustScaler

Scale features using statistics that are robust to outliers.

This Scaler removes the median and scales the data according to the quantile range (defaults to IQR: Interquartile Range). The IQR is the range between the 1st quartile (25th quantile) and the 3rd quartile (75th quantile).

Centering and scaling happen independently on each feature by computing the relevant statistics on the samples in the training set. Median and interquartile range are then stored to be used on later data using the transform method.

Standardization of a dataset is a common preprocessing for many machine learning estimators. Typically this is done by removing the mean and scaling to unit variance. However, outliers can often influence the sample mean / variance in a negative way. In such cases, using the median and the interquartile range often give better results. For an example visualization and comparison to other scalers, refer to Compare RobustScaler with other scalers.

Python Reference

Constructors

new RobustScaler()

new RobustScaler(opts?): RobustScaler

Parameters

Parameter	Type	Description
`opts`?	`object`	-
`opts.copy`?	`boolean`	If `false`, try to avoid a copy and do inplace scaling instead. This is not guaranteed to always work inplace; e.g. if the data is not a NumPy array or scipy.sparse CSR matrix, a copy may still be returned.
`opts.quantile_range`?	`any`	Quantile range used to calculate `scale_`. By default this is equal to the IQR, i.e., `q_min` is the first quantile and `q_max` is the third quantile.
`opts.unit_variance`?	`boolean`	If `true`, scale data so that normally distributed features have a variance of 1. In general, if the difference between the x-values of `q_max` and `q_min` for a standard normal distribution is greater than 1, the dataset will be scaled down. If less than 1, the dataset will be scaled up.
`opts.with_centering`?	`boolean`	If `true`, center the data before scaling. This will cause `transform` to raise an exception when attempted on sparse matrices, because centering them entails building a dense matrix which in common use cases is likely to be too large to fit in memory.
`opts.with_scaling`?	`boolean`	If `true`, scale the data to interquartile range.

Returns RobustScaler

Defined in generated/preprocessing/RobustScaler.ts:27

Properties

Property	Type	Default value	Defined in
`_isDisposed`	`boolean`	`false`	generated/preprocessing/RobustScaler.ts:25
`_isInitialized`	`boolean`	`false`	generated/preprocessing/RobustScaler.ts:24
`_py`	`PythonBridge`	`undefined`	generated/preprocessing/RobustScaler.ts:23
`id`	`string`	`undefined`	generated/preprocessing/RobustScaler.ts:20
`opts`	`any`	`undefined`	generated/preprocessing/RobustScaler.ts:21

Accessors

center_

Get Signature

get center_(): Promise<any>

The median value for each feature in the training set.

Returns Promise<any>

Defined in generated/preprocessing/RobustScaler.ts:385

feature_names_in_

Get Signature

get feature_names_in_(): Promise<ArrayLike>

Names of features seen during fit. Defined only when X has feature names that are all strings.

Returns Promise<ArrayLike>

Defined in generated/preprocessing/RobustScaler.ts:456

n_features_in_

Get Signature

get n_features_in_(): Promise<number>

Number of features seen during fit.

Returns Promise<number>

Defined in generated/preprocessing/RobustScaler.ts:431

py

Get Signature

get py(): PythonBridge

Returns PythonBridge

Set Signature

set py(pythonBridge): void

Parameters

Parameter	Type
`pythonBridge`	`PythonBridge`

Returns void

Defined in generated/preprocessing/RobustScaler.ts:65

scale_

Get Signature

get scale_(): Promise<any>

The (scaled) interquartile range for each feature in the training set.

Returns Promise<any>

Defined in generated/preprocessing/RobustScaler.ts:408

Methods

dispose()

dispose(): Promise<void>

Disposes of the underlying Python resources.

Once dispose() is called, the instance is no longer usable.

Returns Promise<void>

Defined in generated/preprocessing/RobustScaler.ts:117

fit()

fit(opts): Promise<any>

Compute the median and quantiles to be used for scaling.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`ArrayLike`	The data used to compute the median and quantiles used for later scaling along the features axis.
`opts.y`?	`any`	Not used, present here for API consistency by convention.

Returns Promise<any>

Defined in generated/preprocessing/RobustScaler.ts:134

fit_transform()

fit_transform(opts): Promise<any[]>

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

Parameter	Type	Description
`opts`	`object`	-
`opts.fit_params`?	`any`	Additional fit parameters.
`opts.X`?	`ArrayLike`[]	Input samples.
`opts.y`?	`ArrayLike`	Target values (`undefined` for unsupervised transformations).