predictDataprep

this function can be used for autoDataprep objects to generate the same for validation

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

predictDataprep function

<dl><dt>x</dt>
<dd>[autoDataprep object | Required] autoDataprep object for which prediction is desired</dd>
<dt>data</dt>
<dd>[data.frame | Required] prediction data set to prepare the MAR columns</dd></dl>

Arguments

Extract predictions and generate columns from autoDataprep objects — predictDataprep

<dl>

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


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

</dl>

predictDataprep: Extract predictions and generate columns from autoDataprep objects

Description

Usage

Value

Arguments

Examples