featureInfo-class {familiar}

Feature information object.

Description

A featureInfo object contains information for a single feature. This information is used to check data prospectively for consistency and for data preparation. These objects are, for instance, attached to a familiarModel object so that data can be pre-processed in the same way as the development data.

Slots

name

Name of the feature, which by default is the column name of the feature.

set_descriptor

Character string describing the set to which the feature belongs. Currently not used.

feature_type

Describes the feature type, i.e. factor or numeric.

levels

The class levels of categorical features. This is used to check prospective datasets.

ordered

Specifies whether the

distribution

Five-number summary (numeric) or class frequency (categorical).

data_id

Internal identifier for the dataset used to derive the feature information.

run_id

Internal identifier for the specific subset of the dataset used to derive the feature information.

in_signature

Specifies whether the feature is included in the model signature.

in_novelty

Specifies whether the feature is included in the novelty detector.

removed

Specifies whether the feature was removed during pre-processing.

removed_unknown_type

Specifies whether the feature was removed during pre-processing because the type was neither factor nor numeric..

removed_missing_values

Specifies whether the feature was removed during pre-processing because it contained too many missing values.

removed_no_variance

Specifies whether the feature was removed during pre-processing because it did not contain more than 1 unique value.

removed_low_variance

Specifies whether the feature was removed during pre-processing because the variance was too low. Requires applying low_variance as a filter_method.

removed_low_robustness

Specifies whether the feature was removed during pre-processing because it lacks robustness. Requires applying robustness as a filter_method, as well as repeated measurement.

removed_low_importance

Specifies whether the feature was removed during pre-processing because it lacks relevance. Requires applying univariate_test as a filter_method.

fraction_missing

Specifies the fraction of missing values.

robustness

Specifies robustness of the feature, if measured.

univariate_importance

Specifies the univariate p-value of the feature, if measured.

transformation_parameters

Details parameters for power transformation of numeric features.

normalisation_parameters

Details parameters for (global) normalisation of numeric features.

batch_normalisation_parameters

Details parameters for batch normalisation of numeric features.

imputation_parameters

Details parameters or models for imputation of missing values.

cluster_parameters

Details parameters for forming clusters with other features.

required_features

Details features required for clustering or imputation.

familiar_version

Version of the familiar package.