# 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
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.