Are you ready to learn feature engineering for machine learning and data science? You’re in the right place!
Feature engineering is a critical skill for extracting valuable insights from data, and in this quick guide, I’ll break it down into simple, digestible chunks. So, let’s dive right in and get started on your journey to mastering feature extraction!
What Is Feature Engineering?
When you create a machine learning model related to a business or experimental problem, you supply learning data in columns and rows. In the data science and ML development domain, columns are known as the attributes or variables.
Granular data or rows below these columns are known as observations or instances. The columns or attributes are the features in a raw dataset.
These raw features aren’t enough or optimal to train an ML model. To reduce the noise of the collected metadata and maximize unique signals from features, you need to transform or convert metadata columns into functional features through feature engineering.
Example 1: Financial Modeling
For example, in the above image of an example dataset, the columns from A to G are features. Values or text strings in each column along the rows, like names, deposit amount, years of deposit, interest rates, etc., are observations.
In ML modeling, you must delete, add, combine, or transform data to create meaningful features and reduce the size of the overall model training database. This is feature engineering.
In the same dataset mentioned earlier, features like Tenure Total and Interest Amount are unnecessary inputs. These will simply take more space and confuse the ML model. So, you can reduce two features from a total of seven features.
Since the databases in ML models contain thousands of columns and millions of rows, reducing two features impacts the project a lot.
Example 2: AI Music Playlist Maker
Sometimes, you can create a whole new feature out of multiple existing features. Suppose you’re creating an AI model that’ll automatically create a playlist of music and songs according to event, taste, mode, etc.
Now, you collected data on songs and music from various sources and created the following database:
There are seven features in the above database. However, since your target is to train the ML model to decide which song or music is suitable for which event, you can club features like Genre, Rating, Beats, Tempo, and Speed into a new feature called Applicability.
Now, either through expertise or pattern identification, you can combine certain instances of features to determine which song is suitable for which event. For instance, observations like Jazz, 4.9, X3, Y3, and Z1 tell the ML model that the song Cras maximus justo et should be in the user’s playlist if they’re looking for a sleep time song.
Types of Features in Machine Learning
These are data attributes that represent distinct categories or labels. You must use this type to tag qualitative datasets.
#1. Ordinal Categorical Features
Ordinal features have categories with a meaningful order. For example, education levels like High School, Bachelor’s, Master’s, etc., have a clear distinction in the standards, but there are no quantitative differences.
#2. Nominal Categorical Features
Nominal features are categories without any inherent order. Examples could be colors, countries, or types of animals. Also, there are only qualitative differences.
This feature type represents data organized in arrays or lists. Data scientists and ML developers often use Array Features to handle sequences or embed categorical data.
#1. Embedding Array Features
Embedding arrays convert categorical data into dense vectors. It’s commonly used in natural language processing and recommendation systems.
#2. List Array Features
List arrays store sequences of data, such as lists of items in an order or the history of actions.
These ML training features are used to perform mathematical operations since these features represent quantitative data.
#1. Interval Numerical Features
Interval features have consistent intervals between values but no true zero point—for example, temperature monitoring data. Here, zero means freezing temperature, but the attribute is still there.
#2. Ratio Numerical Features
Ratio features have consistent intervals between values and a true zero point. Examples include age, height, and income.
Importance of Feature Engineering in ML and Data Science
Effective feature extraction improves model accuracy, making predictions more reliable and valuable for decision-making.
Careful feature selection eliminates irrelevant or redundant attributes, simplifying models and saving computational resources.
Well-engineered features reveal data patterns, aiding data scientists in understanding complex relationships within the dataset.
Tailoring features to specific algorithms can optimize model performance across various machine-learning methods.
Well-engineered features lead to faster model training and reduced computational costs, streamlining the ML workflow.
Next, we will explore the step-by-step process of feature engineering.
Feature Engineering Process Step-By-Step
Data Collection: The initial step involves gathering the raw data from various sources, such as databases, files, or APIs.
Data Cleaning: Once you’ve got your data, you must clean it by identifying and rectifying any errors, inconsistencies, or outliers.
Handling Missing Values: Missing values can muddle the feature store of the ML model. If you ignore them, your model will be biased. So, you must research more to input the missing values or carefully omit them without affecting the model with bias.
Encoding Categorical Variables: You must convert categorical variables into numerical format for machine learning algorithms.
Scaling and Normalization: Scaling ensures that numerical features are on a consistent scale. It prevents features with large values from dominating the machine-learning model.
Feature Selection: This step helps to identify and retain the most relevant features, reducing dimensionality and improving model efficiency.
Feature Creation: Sometimes, new features can be engineered from existing ones to capture valuable information.
Feature Transformation: Transformation techniques like logarithms or power transforms can make your data more suitable for modeling.
Next, we will discuss feature engineering methods.
Feature Engineering Methods
#1. Principal Component Analysis (PCA)
PCA simplifies complex data by finding new uncorrelated features. These are called principal components. You can use it to reduce dimensionality and improve model performance.
#2. Polynomial Features
Creating polynomial features means adding powers of existing features to capture complex relationships in your data. It helps your model understand non-linear patterns.
#3. Handling Outliers
Outliers are unusual data points that can affect the performance of your models. You must identify and manage outliers to prevent skewed results.
#4. Log Transform
Logarithmic transformation can help you normalize data with a skewed distribution. It reduces the impact of extreme values to make the data more suitable for modeling.
t-SNE is useful for visualizing high-dimensional data. It reduces dimensionality and makes clusters more apparent while preserving data structure.
In this feature extraction method, you represent data points as dots in a lower-dimensional space. Then, you place the similar data points in the original high-dimensional space and are modeled to be close to each other in the lower-dimensional representation.
It differs from other dimensionality reduction methods by preserving the structure and distances between data points.
#6. One-Hot Encoding
One-hot encoding transforms categorical variables into binary format (0 or 1). So, you get new binary columns for each category. One-hot encoding makes categorical data suitable for ML algorithms.
#7. Count Encoding
Count encoding replaces categorical values with the number of times they appear in the dataset. It can capture valuable information from categorical variables.
In this method of feature engineering, you use the frequency or count of each category as a new numerical feature instead of using the original category labels.
#8. Feature Standardization
Features of larger values often dominate features of small values. Thus, the ML model can easily get biased. Standardization prevents such causes of biases in a machine learning model.
The standardization process typically involves the following two common techniques:
Z-Score Standardization: This method transforms each feature so that it has a mean (average) of 0 and a standard deviation of 1. Here, you subtract the mean of the feature from each data point and divide the result by the standard deviation.
Min-Max Scaling: Min-max scaling transforms the data into a specific range, typically between 0 and 1. You can accomplish this by subtracting the minimum value of the feature from each data point and dividing by the range.
Through normalization, numerical features are scaled to a common range, usually between 0 and 1. It maintains the relative differences between values and ensures all features are on a level playing field.
Popular Feature Engineering Tools
Featuretools is an open-source Python framework that automatically creates features from temporal and relational datasets. It can be used with tools you already use to develop ML pipelines.
The solution uses Deep Feature Synthesis to automate feature engineering. It has a library of low-level functions for creating features. Featuretools also has an API, which is also ideal for precise handling of time.
If you are looking for an open-source library that combines multiple decision trees to create a powerful predictive model, go for CatBoost. This solution offers accurate results with default parameters, so you do not have to spend hours fine-tuning the parameters.
CatBoost also lets you use non-numeric factors to improve your training results. With it, you can also expect to get more accurate results and fast predictions.
Feature-Engine is a Python library with multiple transformers and select features that you can use for ML models. The transformers it includes can be used for variable transformation, variable creation, datetime features, preprocessing, categorical encoding, outlier capping or removal, and missing data imputation. It is capable of recognizing numerical, categorical, and datetime variables automatically.
Feature Engineering Learning Resources
Online Courses and Virtual Classes
#1. Feature Engineering for Machine Learning in Python: Datacamp
#2. Feature Engineering for Machine Learning: Udemy
From the Feature Engineering for Machine Learning course, you will learn topics including imputation, variable encoding, feature extraction, discretization, datetime functionality, outliers, etc. Participants will also learn to work with skewed variables and deal with infrequent, unseen, and rare categories.
#3. Feature Engineering: Pluralsight
This Pluralsight learning path has a total of six courses. These courses will help you learn the importance of feature engineering in ML workflow, ways to apply its techniques, and feature extraction from text and images.
#4. Feature Selection for Machine Learning: Udemy
With the help of this Udemy course, participants can learn feature shuffling, filter, wrapper, and embedded methods, recursive feature elimination, and exhaustive search. It also discusses feature selection techniques, including the ones with Python, Lasso, and decision trees. This course contains 5.5 hours of on-demand video and 22 articles.
#5. Feature Engineering for Machine Learning: Great Learning
This course from Great Learning will introduce you to feature engineering while teaching you about over-sampling and under-sampling. Furthermore, it will let you perform hands-on exercises on model tuning.
#6. Feature Engineering: Coursera
Join the Coursera course to use BigQuery ML, Keras, and TensorFlow to perform feature engineering. This intermediate-level course also covers advanced feature engineering practices.
Digital or Hardcover Books
#1. Feature Engineering for Machine Learning
This book teaches you how to transform features into formats for machine-learning models.
The book uses a cross-domain approach to discuss graphs, texts, time series, images, and case studies.
So, this is how you can perform feature engineering. Now that you know the definition, stepwise process, methods, and learning resources, you can implement these into your ML projects and see the success!
Tamal is a freelance writer at Geekflare. After completing his MS in Science, he joined reputed IT consultancy companies to acquire hands-on knowledge of IT technologies and business management. Now, he’s a professional freelance content… read more