Must-Know ML Feature Engineering Techniques in 2024

We are entering a unique era in mankind's history. In 2023, generative AI emerged, paving the way for new advancements in machine learning. As we move into 2024 and beyond, ML models are becoming even more powerful, with outcomes that closely mimic or even exceed the accuracy of human-generated results.

According to Statista's forecast, the number of AI-powered digital voice assistant units will reach 8.4 billion by 2024, surpassing the world's population.

In the realm of AI and Machine Learning (ML), feature engineering stands as a crucial step towards building high-performing models. It involves the process of transforming raw data into meaningful features that can be readily understood and utilized by machine learning algorithms.

By carefully crafting informative features, data scientists can significantly enhance the accuracy, interpretability, and efficiency of their models.  

Let's dig deeper into ML feature engineering and the various techniques that will make an impact in 2024.

What is ML Feature Engineering?

If you are familiar with ML models, you will know that the key to training and deploying efficient ML algorithms lies in the data that is used to train this system.

Suppose the data contains inaccuracies or isn’t in the right format. In that case, it can create anomalies in the ML model that may not be noticed initially but can be detrimental to an ML implementation.

ML feature engineering is the process of transforming raw data into meaningful features that can be readily understood and utilized by ML algorithms. It encompasses a wide range of techniques, including data cleaning, feature selection, feature extraction, and feature creation.

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Importance of ML Feature Engineering

ML feature engineering plays a pivotal role in the success of ML projects for several reasons:

1. Improved Model Performance: Well-engineered features provide the model with more relevant and discriminative information, enabling it to make more accurate predictions.
2. Enhanced Interpretability: ML feature engineering can simplify complex relationships within the data, making it easier for both data scientists and stakeholders to interpret and understand the model's decisions.
3. Handling Missing Data and Outliers: Proper handling of missing values and outliers ensures that the data accurately represents the underlying patterns and relationships.
4. Noise Reduction: By focusing on the most informative aspects, feature engineering helps to filter out irrelevant or noisy information, leading to more robust and generalizable models.
5. Addressing Non-Linearity: ML models often struggle with non-linear relationships. For example, a company productivity-enhancing ML model needs to match the time taken to complete a task with the number of people working on it. Feature engineering captures these intricate patterns better.

Top 9 Techniques for Enhancing Features in ML

Now that you understand the essence of ML feature engineering, let's explore the top techniques that are shaping the landscape in 2024.

1. Exploratory Data Analysis (EDA)

EDA is an initial step in feature engineering, which allows data scientists to analyze visual and statistical data and gain insights into relationships, patterns, and potential issues that guide subsequent feature engineering decisions.

Consider an eCommerce company determining how much inventory it should have for an upcoming holiday. The firm has the following data: daily sales, stock levels, and the number of orders during the holiday season over the past few years.

Using exploratory data analysis, the company can understand the relationships between the increase in orders and stock levels, helping it gain insights into customer behavior, sales patterns, and inventory dynamics. This helps them plan better, and make strategic decisions over overall business efficiency.

Using a no-code platform like MarkovML, you can quickly perform exploratory data analysis on your dataset and determine its suitability for machine learning model generation.

2. Missing Data Imputation

Incomplete or missing data can distort the true representation of data. Imputation techniques, such as mean imputation, median imputation, or k-nearest neighbors imputation, are employed to fill in missing values with plausible estimates.

For instance, consider a scenario where you are predicting property prices in a certain area. Missing data on key features can hinder model training and prediction accuracy. By employing imputation techniques, such as estimating missing values based on available data like property area, the ML model can make more informed predictions, ensuring a more robust and reliable outcome.

3. Feature Scaling

Feature scaling is a critical preprocessing step in machine learning that normalizes the range of features, ensuring they contribute equally to the model's learning process.

For instance, consider training a model to predict house prices using various features such as "square footage," "number of bedrooms," and "distance to amenities."

Without feature scaling, features with larger scales, such as square footage, might dominate the model's learning process, overshadowing the importance of other features like the number of bedrooms. By employing techniques like Min-Max scaling or Standardization, you can bring all features to a similar scale, allowing the model to learn from each feature effectively and making predictions more accurate and reliable.

4. One-Hot Encoding and Categorical Feature Transformation

One-hot encoding transforms categorical variables into binary vectors, enabling machine learning algorithms to process categorical data effectively. Other techniques, such as binary encoding and label encoding, can also be employed.

5. Feature Selection Techniques

Feature selection involves identifying and selecting the most relevant and informative features, reducing dimensionality, and enhancing model performance.

For instance, to identify the genes linked to a disease, Recursive Feature Elimination (RFE) can remove the least informative genes until a desired model performance is achieved. Then, using the top genes, tree-based models can predict the likelihood of the disease occurrence with greater accuracy and precision.

6. Dimensionality Reduction

Techniques of Dimensionality Reduction aim to reduce the number of features while preserving the essential information in the data.

For example, analyzing financial data with features for every stock price movement can be overwhelming. To make this process simpler, you can zero in on stocks that match a particular pattern, such as Y-O-Y profits, funds in balance, and other critical criteria to determine the best stocks.

Using dimensionality reduction, you can identify the most significant underlying "trends" in the data, potentially reducing hundreds of features to just a few principal components, simplifying the model without sacrificing accuracy.

7. Feature Engineering for Time Series Data

Due to its temporal dependency, time series data poses unique challenges. Techniques like feature extraction, feature generation, and temporal aggregation transform time series data into a format suitable for machine learning algorithms.

Predicting website traffic over time is typically an example of this. Using time series data, you can analyze events that cause huge fluctuations in users, such as daily trends, seasonal patterns, and holiday spikes.

Feature generation could create new features like "day of the week" or "week of the month" to capture these cyclical patterns, helping organizations make data-driven decisions on improving their servers and reducing downtimes.

8. Text Data Feature Engineering

Text data requires specialized techniques like tokenization, stemming, lemmatization, and n-gram generation to extract meaningful features from unstructured text.

An example of this can be seen in movie review analysis, where sentiment analysis helps us derive meaningful features from unstructured text. Tokenization splits the text into individual words while stemming reduces them to their root forms ("running," "ran").

N-grams create sequences of words ("great movie," "loved the acting"), capturing broader context for sentiment analysis.

9. Advanced Techniques

ML feature engineering also encompasses a vast array of advanced techniques, including feature interaction, feature hashing, and kernel methods. These techniques are often employed in complex ML domains to discover valuable features automatically.

For example,  when training an ML model to analyze weather data to predict crop yields for a particular year, you can use feature casing to efficiently encode complex interactions between features like temperature and rainfall, potentially revealing hidden patterns impacting crop growth.

Applications of Feature Engineering in ML

ML feature engineering has diverse applications and can be used in several functions or industries where ML is implemented. These include:

• Fraud Detection: Feature engineering helps identify patterns and anomalies in financial transactions to detect fraudulent activities.
  
• Medical Diagnosis: Feature extraction from medical images and patient data aids in accurate disease diagnosis and risk assessment.
• Recommendation Systems: ML feature engineering is vital for recommendation systems, like those used by Netflix or Amazon. These systems analyze user data to offer personalized suggestions, such as what to watch next or which book to buy, enhancing user experience and engagement.
• Natural Language Processing (NLP): Feature engineering can be used in NLP applications for tasks like sentiment analysis, machine translation, and text summarization.
• Image Recognition: Feature extraction from images enables object detection, facial recognition, and other image-based applications.

Conclusion

ML feature engineering is pivotal for enhancing the predictive power of machine learning models by refining raw data into actionable insights. By mastering feature engineering techniques, data scientists can unlock the true potential of data, driving innovation and solving real-world problems across various industries.

With MarkovML, a no-code platform, you can streamline your ML workflows and effortlessly harness the power of feature engineering to drive innovation and solve real-world challenges.

Want to see how MarkovML can revolutionize your ML journey? Book a demo today and discover how our platform simplifies feature engineering and accelerates model development.

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