run_validation_tests.numeric

<code>list</code> statistics and meta data of the same variable
recorded from training data (with <code><a rd-options="" href="/link/record?package=recorder&version=0.8.2" data-mini-rdoc="recorder::record">record</a></code>).

parameters

further arguments passed to or from other methods. Not used at
the moment.

Runs a set of validation tests on a <code>numeric</code> in new data. These tests
are based on statistics and meta data of the same variable recorded 
(with <code><a rd-options="" href="/link/record?package=recorder&version=0.8.2" data-mini-rdoc="recorder::record">record</a></code>) from the training 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.

Lars Kjeldgaard

recorder

Toolkit to Validate New Data for a Predictive Model

run_validation_tests.numeric function

<code>list</code> statistics and meta data of the same variable
recorded from training data (with <code><a rd-options='' href='record'>record</a></code>).

Runs a set of validation tests on a <code>numeric</code> in new data. These tests
are based on statistics and meta data of the same variable recorded 
(with <code><a rd-options='' href='record'>record</a></code>) from the training data.

run_validation_tests.numeric: Run Validation Tests on a Numeric

Description

Usage

Arguments

Value