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DriveML  

Self-Drive Machine Learning Projects
View on CRAN: Click here


Download and install DriveML package within the R console
Install from CRAN:
install.packages("DriveML")

Install from Github:
library("remotes")
install_github("cran/DriveML")

Install by package version:
library("remotes")
install_version("DriveML", "0.1.5")



Attach the package and use:
library("DriveML")
Maintained by
Dayanand Ubrangala
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2020-06-04
Latest Update: 2023-04-18
Description:
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) , [2] Sperandei S (2014) , [3] Breiman L (2001) , [4] Kingsford C and Salzberg S (2008) , [5] Chen Tianqi and Guestrin Carlos (2016) .
How to cite:
Dayanand Ubrangala (2020). DriveML: Self-Drive Machine Learning Projects. R package version 0.1.5, https://cran.r-project.org/web/packages/DriveML. Accessed 21 Nov. 2024.
Previous versions and publish date:
0.1.0 (2020-06-04 11:50), 0.1.2 (2021-03-09 10:10), 0.1.3 (2021-06-14 10:20), 0.1.4 (2021-10-18 13:10), 0.1.5 (2022-12-02 12:20)
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