elphick.sklearn_viz.features.outlier_detection.OutlierDetection

class elphick.sklearn_viz.features.outlier_detection.OutlierDetection(x, pca_spec=0, standardise=False, p_val=0.001)[source]

__init__(x, pca_spec=0, standardise=False, p_val=0.001)[source]

Parameters:

x (DataFrame) – X values for outlier detection.
pca_spec (Union[float, int]) – If zero, pca is not used. For integers (n) > 0 outlier detection is performed on the top n principal components. For values (f) < 1, outlier detection is performed on the number of principal components that explain f% of the variance.
standardise (bool) – If True, standardise the data prior to PCA, where vectors are transformed to zero mean and unit variance.
p_val (float) – the p-value threshold for outlier detection.

Methods

__init__(x[, pca_spec, standardise, p_val])

plot_outlier_matrix([principal_components])

Attributes

data