misspattern

This function is used to summarise the missing variable, missing pattern identification and classifying the columns based on the pattern of missing values.

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

misspattern function

<dl><dt>data</dt>
<dd>[data.frame | Required] data set with missing values</dd>
<dt>mfeature</dt>
<dd>[character | Required] only missing variable name</dd>
<dt>drop</dt>
<dd>[numeric | optional] drop variable percentage. Example, if drop = 0.9, function will automatically drop 90per missing columns from the data set</dd>
<dt>print</dt>
<dd>[character | optional] defualt print is FALSE</dd></dl>

Arguments

Missing pattern analysis for missing data — misspattern

<dl>

<dt>data</dt>
<dd>[data.frame | Required] data set with missing values</dd>


<dt>mfeature</dt>
<dd>[character | Required] only missing variable name</dd>


<dt>drop</dt>
<dd>[numeric | optional] drop variable percentage. Example, if drop = 0.9, function will automatically drop 90per missing columns from the data set</dd>


<dt>print</dt>
<dd>[character | optional] defualt print is FALSE</dd>

</dl>

misspattern: Missing pattern analysis for missing data

Description

Usage

Value

Arguments

Examples