predictAutoMAR

this function can be used for autoMAR objects to generate the variable for missing variable not completely at random

Implementing some of the pillars of an automated machine learning pipeline such as (i) Automated data preparation, (ii) Feature engineering, (iii) Model building in classification context that includes techniques such as (a) Regularised regression [1], (b) Logistic regression [2], (c) Random Forest [3], (d) Decision tree [4] and (e) Extreme Gradient Boosting (xgboost) [5], and finally, (iv) Model explanation (using lift chart and partial dependency plots). Accomplishes the above tasks by running the function instead of writing lengthy R codes. Also provides some additional features such as generating missing at random (MAR) variables and automated exploratory data analysis. Moreover, function exports the model results with the required plots in an HTML vignette report format that follows the best practices of the industry and the academia. [1] Gonzales G B and De Saeger (2018) <doi:10.1038/s41598-018-21851-7>, [2] Sperandei S (2014) <doi:10.11613/BM.2014.003>, [3] Breiman L (2001) <doi:10.1023/A:1010933404324>, [4] Kingsford C and Salzberg S (2008) <doi:10.1038/nbt0908-1011>, [5] Chen Tianqi and Guestrin Carlos (2016) <doi:10.1145/2939672.2939785>.

Dayanand Ubrangala

DriveML

Self-Drive Machine Learning Projects

Dayanand Ubrangala 

Sayan Putatunda

Kiran R

Ravi Prasad Kondapalli

predictAutoMAR function

<dl><dt>x</dt>
<dd>[autoMAR object | Required] autoMAR object for which prediction is desired</dd>
<dt>data</dt>
<dd>[data.frame | Required] prediction data set to prepare the autoMAR outcomes</dd>
<dt>mar_var</dt>
<dd>[character list | Optional] list of predefined mar variables</dd></dl>

Arguments

Extract predictions and MAR columns from autoMAR objects — predictAutoMAR

<dl>

<dt>x</dt>
<dd>[autoMAR object | Required] autoMAR object for which prediction is desired</dd>


<dt>data</dt>
<dd>[data.frame | Required] prediction data set to prepare the autoMAR outcomes</dd>


<dt>mar_var</dt>
<dd>[character list | Optional] list of predefined mar variables</dd>

</dl>

predictAutoMAR: Extract predictions and MAR columns from autoMAR objects

Description

Usage

Value

Arguments

Examples