VarianceThreshold

Removes low-variance features.

Parameters

• threshold

  Default → 0

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

• min_samples

  Default → 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.