Introduction

In any machine learning project, the secret to building a powerful model lies not in the algorithm itself but in the quality and preparation of the data that feeds it. Raw datasets often arrive messy, incomplete, or inconsistent. Before an algorithm can make sense of them, they must be cleaned, reshaped, and enhanced — a process known as feature engineering and preprocessing.

This stage transforms unrefined data into meaningful, machine-readable inputs that strengthen a model’s learning capacity and improve its generalization performance. Within the tidymodels framework in R, the recipes package offers a structured and reproducible way to carry out these transformations step by step.

Understanding Feature Engineering

Feature engineering is the creative and analytical process of turning raw data into informative variables, or features, that capture the essence of a problem. Think of it as sculpting — removing noise and refining structure until the important patterns become visible to the model.

It usually involves:

• Cleaning and formatting inconsistent or missing data
• Encoding categorical variables into numeric representations
• Normalizing or standardizing numerical features
• Handling outliers and skewed data
• Constructing new features based on domain knowledge or relationships within the data

For example, in a housing dataset, creating a feature like “price per square meter” or “age of the building” might reveal stronger predictive relationships than raw variables like price or year built alone.

Designing a Preprocessing Pipeline

The recipes package provides a consistent framework to define, document, and apply transformations in a reproducible sequence. A recipe can be thought of as a blueprint for your data workflow — every step you define is applied in order, ensuring no manual changes are lost or misapplied later.

Each recipe is composed of:

1. Steps – transformation operations such as imputing, encoding, or scaling
2. Prep phase – training the recipe on your training data to estimate required parameters (e.g., mean, median)
3. Bake phase – applying those same transformations to both training and new data

This structure ensures that preprocessing remains consistent across all stages of model development.

Example: Cleaning and Preparing Vehicle Data

Suppose you’re analyzing a dataset that predicts car fuel efficiency (mpg) based on various numeric and categorical features. Some data points are missing, and the scale of each numeric column differs.

library(recipes)

data(mtcars)

car_recipe <- recipe(mpg ~ ., data = mtcars) %>%

  step_impute_mean(all_numeric_predictors()) %>%

  step_dummy(all_nominal_predictors()) %>%

  step_normalize(all_numeric_predictors())

Here’s what happens:

1. Missing numeric values are replaced with the mean.
2. Categorical variables are converted into binary dummy indicators.
3. Numeric predictors are standardized (mean = 0, SD = 1).

Once defined, the recipe is “prepped” and “baked”:

prepared <- prep(car_recipe, training = mtcars)

clean_data <- bake(prepared, new_data = mtcars)

This ensures that the exact same transformations are applied to any future data without manual intervention — a key principle for reliable modeling.

Managing Missing Values

Missing data is one of the most common challenges in analytics. The right approach depends on the data type and the reason values are missing.

Common strategies include:

• Mean or median imputation for numerical columns
• Mode imputation for categorical data
• Predictive imputation using nearest neighbors (step_knnimpute()) or bagging models (step_bagimpute())

Example:

recipe(mpg ~ ., data = mtcars) %>%

  step_impute_median(all_numeric_predictors())

This method replaces missing values with the median, providing a more robust estimate when data contains outliers.

Encoding Categorical Variables

Since most machine learning algorithms require numerical input, categorical variables must be encoded.

• One-Hot Encoding (step_dummy()): Creates binary columns for each category.
• Lumping (step_other()): Combines rare categories into an “other” group to prevent overfitting.
• Ordinal Encoding (step_integer()): Assigns integers to categories when order matters (e.g., education level).

Example:

recipe(mpg ~ ., data = mtcars) %>%

  step_dummy(all_nominal_predictors())

When working with high-cardinality features — like ZIP codes or product IDs — combining less frequent values into a single category can improve model efficiency and reduce noise.

Scaling and Normalization

Features with vastly different scales can bias many algorithms, especially those that rely on distance metrics such as KNN or gradient descent.

Normalization (step_normalize()) rescales variables so that each has a mean of 0 and a standard deviation of 1:

recipe(mpg ~ ., data = mtcars) %>%

  step_normalize(all_numeric_predictors())

Other transformations include:

• step_log() for compressing right-skewed data (e.g., income)
• step_range() for scaling to a specific range (e.g., [0, 1])

These adjustments ensure that every feature contributes fairly to the model’s learning process.

Creating New and Derived Features

Domain expertise often unlocks new predictive power through engineered features. Analysts can create:

• Interaction terms to model relationships between two variables
• Aggregated statistics like averages, ratios, or rolling means
• Temporal features such as day of week, quarter, or time since last event

Example:

recipe(mpg ~ ., data = mtcars) %>%

  step_interact(~ disp:hp)

This creates a feature representing the interaction between engine displacement and horsepower — a potential indicator of vehicle efficiency.

Dealing with Outliers

Outliers can distort model results, especially in regression and clustering tasks. Using step_clip() or custom transformations, extreme values can be limited or transformed:

recipe(mpg ~ ., data = mtcars) %>%

  step_clip(all_numeric_predictors(), lower = 0, upper = 400)

This caps variable values to a defined range, improving model stability and preventing extreme cases from dominating the learning process.

Ensuring Reproducibility and Workflow Integration

A major advantage of using the recipes package is traceability. Every transformation is recorded, allowing teams to reproduce results exactly — a critical requirement in professional and regulatory environments.

These recipes can easily integrate into a broader modeling workflow:

library(workflows)

library(parsnip)

model_spec <- linear_reg() %>% set_engine(“lm”)

wf <- workflow() %>%

  add_recipe(car_recipe) %>%

  add_model(model_spec)

fit <- fit(wf, data = mtcars)

Here, preprocessing and modeling are tightly coupled, ensuring the model always receives data prepared in the same way.

Conclusion

Feature engineering and preprocessing form the backbone of every successful machine learning project. Without thoughtful data preparation, even the most advanced algorithm will fail to perform consistently.

By leveraging R’s recipes package, data scientists can build structured, repeatable, and auditable pipelines that handle every stage — from cleaning and encoding to scaling and feature creation.
This not only improves model accuracy but also enhances collaboration, transparency, and long-term maintainability.

In short, mastering feature engineering is less about memorizing functions and more about understanding data deeply — refining it until it tells its story clearly and truthfully to the model.