Toolkit to Validate New Data for a Predictive Model
Compress Results of Detailed Tests
Concatenate Validation Test Failures Descriptions
Create Data Frame with Test Results
Create Meta Data of Validation Tests
Get Clean Rows
Get Failed Tests
Get Failed Tests as a String
Get Meta Data of Validation Tests in a Data Frame
Ignore Certain Test Results
Ignore Test Results from Tests of Specific Columns
Ignore Test Results from Specific Tests of Specific Columns
Ignore Results from Specific Tests
Order Test Results by Test Names
Validate New Data by Playing a Data Tape on It
Print Data Playback
Record Statistics and Meta Data of a Character
Record Statistics and Meta Data of a Data Frame
Record Statistics and Meta Data
Record Statistics and Meta Data of a Factor
Record Statistics and Meta Data of an Integer
Record Statistics and Meta Data of a Numeric
Record Statistics and Meta Data of Variables in Training Data
Run Validation Tests on Character
Run Validation Tests on Variable
Run Validation Tests on Factor
Run Validation Tests on Integer
Run Validation Tests on a Numeric
Run Validation Tests on Variable in New Data
A lightweight toolkit to validate new observations when computing their predictions with a predictive model. The validation process consists of two steps: (1) record relevant statistics and meta data of the variables in the original training data for the predictive model and (2) use these data to run a set of basic validation tests on the new set of observations.