# VarianceThreshold¶

Removes low-variance features.

## Parameters¶

• threshold – defaults to 0

Only features with a variance above the threshold will be kept.

• min_samples – defaults to 2

The minimum number of samples required to perform selection.

## Attributes¶

• variances (dict)

The variance of each feature.

## Examples¶

>>> from river import feature_selection
>>> from river import stream

>>> X = [
...     [0, 2, 0, 3],
...     [0, 1, 4, 3],
...     [0, 1, 1, 3]
... ]

>>> selector = feature_selection.VarianceThreshold()

>>> for x, _ in stream.iter_array(X):
...     print(selector.learn_one(x).transform_one(x))
{0: 0, 1: 2, 2: 0, 3: 3}
{1: 1, 2: 4}
{1: 1, 2: 1}


## Methods¶

check_feature
clone

Return a fresh estimator with the same parameters.

The clone has the same parameters but has not been updated with any data. This works by looking at the parameters from the class signature. Each parameter is either - recursively cloned if it's a River classes. - deep-copied via copy.deepcopy if not. If the calling object is stochastic (i.e. it accepts a seed parameter) and has not been seeded, then the clone will not be idempotent. Indeed, this method's purpose if simply to return a new instance with the same input parameters.

learn_one

Update with a set of features x.

A lot of transformers don't actually have to do anything during the learn_one step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the learn_one can override this method.

Parameters

• x (dict)

Returns

Transformer: self

transform_one

Transform a set of features x.

Parameters

• x (dict)

Returns

dict: The transformed values.