How to Design a Feature Engineering Pipeline

A Feature Engineering (FE) pipeline should be designed based on the characteristics of your source data and the objective of your machine learning model. Instead of adding all the available nodes, select only the nodes that improve the quality and usefulness of the data for your intended downstream task.

This section explains how to evaluate your data, identify the required transformations, and choose the appropriate Feature Engineering nodes.

Understanding your source data

Determining the downstream objective

Selecting the processing nodes

Understanding your source data

Before selecting any Feature Engineering techniques, you must examine the source data to understand its quality and structure.

Review the dataset for the following characteristics:

Data quality

Identify issues that may affect the performance of your ML models, such as:

Missing values
Duplicate records
Invalid or inconsistent values
Incorrect data types
Empty columns
High-cardinality categorical columns

Data distribution

Analyze the distribution of numerical features to determine whether:

Features have significantly different ranges
Outliers are present
The data is skewed
Features require normalization or scaling

Data Categories

Different data categories require different preprocessing techniques. So it is important to determine the types of data categories available in the dataset:

Numerical
Categorical
Date and time
Text
Boolean

Target column

If you are building a supervised machine learning model, identify the target variable (also called the target column). This is the column whose value the model is trained to predict.

For example:

Customer ID	Age	Monthly Spend	Churn
C101	35	4500	Yes
C102	42	6200	No
C103	28	3100	Yes

In this dataset, Churn is the target variable because the model is trained to predict whether a customer will leave (Yes or No).

After identifying the target variable, consider the following:

Are the target values balanced?

Check whether each target value has a similar number of records. For example, if 95% of customers have No for churn and only 5% have Yes, the data is imbalanced. In such cases, you may need to use an Imbalance Handling node before training the model.
What type of prediction are you making?

Determine whether the model is predicting:
- A category (Classification): For example, predicting whether a customer will churn (Yes/No) or whether a transaction is Fraud/Not Fraud.
- A numeric value (Regression): For example, predicting the house price, sales amount, or temperature.
Does the target variable require preprocessing?

Some target variables may need preprocessing before training. For example, categorical values such as High, Medium, and Low may need to be encoded into numerical values. Similarly, if the target variable contains missing values, you may need to clean or impute the data before training

Determining the downstream objective

The design of a Feature Engineering pipeline should support the intended machine learning objective. Every pipeline may not require all processing steps. Include only the nodes that improve the suitability of the data for the selected model.

For example:

Objective	Feature Engineering Activities
Improve data quality	Data Cleaning, Imputation, Duplicate Removal
Prepare data for machine learning	Encoding, Scaling, Outlier Handling
Handle class imbalance	SMOTE, Oversampling, Under sampling
Create more predictive features	Feature Creation, Feature Selection
Combine data from multiple sources	Join
Prepare data for model evaluation	Train/Test Split or Train/Validation/Test Split

Selecting the processing nodes

Here is a decision matrix that can help you decide which nodes to add to your Feature Engineering pipeline.

If your dataset ...	Use…..
Contains missing values	Imputation
Contains duplicate or inconsistent data	Data Cleaning
Contains categorical columns	Encoding
Contains features with different numerical ranges	Scaling
Contains extreme values	Outlier Handling
Has an imbalanced target variable	Imbalance Handling
Requires calculated features	Feature Creation
Combines multiple datasets	Join
Must be divided for model training	Train/Test Split or Train/Validation/Test Split