Introduction to Custom Steps in Recipes for Machine Learning Model Training

The recipes package in R is a powerful tool for building predictive models. It allows users to define custom steps that can be applied to their data before or after the model training process. In this article, we will explore how to use recipes with custom step functions and address common issues encountered while using them.

Background on Recipes Package

The recipes package in R is a wrapper around other popular machine learning packages like caret, e1071, and wavelets. It provides an easy-to-use interface for building predictive models by allowing users to define custom steps that can be applied to their data. The recipe object represents the complete pipeline of preprocessing steps required for a model.

Defining Custom Step Functions

One of the key features of the recipes package is its ability to define custom step functions. These functions can be used to perform complex data preprocessing tasks, such as feature engineering or data transformation. In this article, we will focus on defining custom step functions for machine learning model training.

Defining Custom Step Functions using Recipes

To define a custom step function in R recipes, you need to create a function that takes the recipe object and other parameters as input. The function should return an updated recipe object with the desired preprocessing steps applied.

Here is an example of how to define a custom step function:

# Define the custom step function
step_Haar_new <- function(terms, role, trained, skip, columns, id) {
  # Create a new step class that inherits from "Haar"
  step(subclass = "Haar", terms = terms, role = role, 
       trained = trained, skip = skip, columns = columns, id = id)
}

Creating Custom Step Functions for Model Training

To create custom step functions for model training, you need to define the preprocessing steps that will be applied before or after the model training process. These steps can include data transformation, feature engineering, or other complex tasks.

Here is an example of how to create a custom step function for transforming data using the Haar algorithm:

# Define the Haar transform function
HaarTransform <- function(DF1) {
  # Apply the Haar transform to each column in the dataframe
  w <- function(k) { 
    s1 <- dwt(k, filter = "haar")
    return (s1@V[[1]])
  }
  Smt <- as.matrix(DF1)
  Smt <- t(base::apply(Smt, 1, w))
  return (data.frame(Smt))
}

# Create a custom step function that applies the Haar transform
step_Haar <- function(recipe, ..., role = "predictor", trained = FALSE, skip = FALSE, 
                    columns = NULL, id = rand_id("Harr")) {
  # Define the preprocessing steps to be applied
  terms <- ellipse_check(...)
  
  # Add the step to the recipe object
  add_step(recipe, 
           step_Haar_new(terms = terms, role = role, trained = trained, 
                         skip = skip, columns = columns, id = id))
}

Creating a Custom Recipe Object

To create a custom recipe object that includes multiple preprocessing steps, you need to define the individual step functions and then add them to the recipe object.

Here is an example of how to create a custom recipe object:

# Define the custom recipe object
Haar_recipe <- recipe(carbon ~ ., biomass) %>% 
  step_Haar(all_predictors()) 

# Test the recipe function
Haar_recipe %>%
  prep(biomass) %>% 
  bake(biomass)

Fitting a Machine Learning Model with Custom Recipe Object

To fit a machine learning model using a custom recipe object, you need to pass the recipe object and other parameters to the caret::train() function.

Here is an example of how to fit a machine learning model using a custom recipe object:

# Fit the caret model
fit <- caret::train(Haar_recipe, data = biomass, method = "svmLinear")  

Conclusion

In this article, we explored how to use recipes with custom step functions for machine learning model training. We discussed the importance of defining custom preprocessing steps and provided examples of how to create custom step functions using R recipes.

Last modified on 2024-07-26