Build and Tune Several Models
Add predictions to the data set. A dplyr compatible way to add predict...
Add model residuals
A convenient way to perform grouped operations
Drops non numeric columns from a data.frame object
Extract important model attributes
Fit and predict in a single function.
Fit several models with different response variables
A pipe friendly way to get summary stats for exploratory data analysis
Get the exponent of any number or numbers
A convenience function that returns the mode
Helper function to easily access elements
Get correlations for combinations
Get correlations between variables
Simultaneously train and predict on new data.
Fit and predict in one function
Replace NAs by group
Replace missing values
Plot a correlations matrix
Create a simplified report of a model's summary
Get row differences between values
A convenient selector gadget
Get the row corresponding to a given percentile
Frequently one needs a convenient way to build and tune several models in one go.The goal is to provide a number of machine learning convenience functions. It provides the ability to build, tune and obtain predictions of several models in one function. The models are built using functions from 'caret' with easier to read syntax. Kuhn(2014) <doi:10.48550/arXiv.1405.6974>.
Useful links