df_model_monitoring_distributions: Bin and calculate feature distributions for model monitoring

In BrandonRCopeland/DataScience: Provides Helper Functions for Data Science Analysis

Bin and calculate feature distributions for model monitoring

Description

This function takes two matrices as input. One should contain the features with their expected values. The other should contain the features with their actual values. Example... if we're comparing Oct '18 to Nov '18 features, Oct '18 would be expected and Nov '18 would be actual.

Usage

df_model_monitoring_distributions(expected, actual, features)

Arguments

expected	Required: A matrix containing features with the expected (old) data.
actual	Required: A matrix containing features from with the actual (new) data.
features	Optional: A vector of the feature names to validate. Note, the feature names must exist in both expected_ and actual_ and be of the same data type in each data frame. If not features are provided, all features in expected_ will be used.

Details

NOTE: This function currently only supports NUMERIC and/or CHARACTER datatypes. Furthermore, bins with less than 5 occurances will be combined into an "other" bucket.

Value

A matrix containing the feature name, bin, min value, max value, expected count, expected

BrandonRCopeland/DataScience documentation built on Oct. 14, 2023, 9:45 a.m.