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hub / github.com/epsilon-machine/missingpy / KNNImputer

Class KNNImputer

missingpy/knnimpute.py:25–328  ·  view source on GitHub ↗

Imputation for completing missing values using k-Nearest Neighbors. Each sample's missing values are imputed using values from ``n_neighbors`` nearest neighbors found in the training set. Each missing feature is then imputed as the average, either weighted or unweighted, of these neighb

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Callers 12

test_knn_imputation_zeroFunction · 0.90
test_knn_n_neighborsFunction · 0.90
test_weight_uniformFunction · 0.90
test_weight_distanceFunction · 0.90
test_metric_typeFunction · 0.90
test_callable_metricFunction · 0.90
test_complete_featuresFunction · 0.90

Calls

no outgoing calls

Tested by 12

test_knn_imputation_zeroFunction · 0.72
test_knn_n_neighborsFunction · 0.72
test_weight_uniformFunction · 0.72
test_weight_distanceFunction · 0.72
test_metric_typeFunction · 0.72
test_callable_metricFunction · 0.72
test_complete_featuresFunction · 0.72