analysis_nas

A data.frame with independent variables and target variable.

Logical, nas analysis of the nominal variables. Default is TRUE.

class_var

A list contains nas rate of each variable.

nas_rate

Names of variables which contain nas.

na_vars

A shadow matrix of variables which contain nas.

mat_nas_shadow

A data.frame with random nas imputation.

dt_nas_random

#' <code>analysis_nas</code> is for understanding the reason for missing data and understand distribution of missing data so we can categorise it as:<ul>
<li>Missing completely at random(MCAR)</li>
<li>MMissing at random(MAR), or</li>
<li>Missing not at random, also known as IM.</li>
</ul>


Provides a toolkit for building predictive models in one integrated offering. Contains infrastructure functionalities such as data exploration and preparation, missing values treatment, outliers treatment, variable derivation, variable selection, dimensionality reduction, grid search for hyperparameters, data mining and visualization, model evaluation, strategy analysis etc. 'creditmodel' is designed to make the development of binary classification models (machine learning based models as well as credit scorecard) simpler and faster.
The references including:
1.Anderson, R. (2007). The credit scoring toolkit: Theory and practice for retail credit risk management and decision automation.
2.Find, S. (2012, ISBN13: 9780230347762). Credit scoring, response modelling and insurance rating:A practical guide to forecasting consumer behaviour.

analysis_nas: Missing Analysis

Description

Usage

Arguments

Value