Is Your Machine Learning Model(Types of ML Models) Underperforming?Feature engineering might be the missing piece. No matter how advanced your algorithm is,poor-quality data can limit its potential. If your model struggles with accuracy, overfitting, orslow training times, chances are your features(What Are Features in Machine Learning?)need serious improvement. Without the right features, even the best models fail to delivermeaningful insights.This is where the real challenge begins. Raw data is messy. It’s filled with noise, irrelevantvariables, and missing values. Worse, bad features can mislead your model, makingpredictions unreliable. If you don’t transform your data properly, your model will struggle. Andwhen your model struggles, so do your results. This means wasted resources, inaccurateforecasts, and lost opportunities.The solution? Master feature engineering. By creating, transforming, and selecting the rightfeatures, you can supercharge your model’s performance. It’s not just about data; it’s aboutsmarter data. Let’s explore how feature engineering can make all the difference.l.toLowerCase().replace(/\s+/g,"-")" id="be738054-1b73-400a-abfa-a1f82f0852c7" data-toc-id="be738054-1b73-400a-abfa-a1f82f0852c7">What is Feature Engineering?Feature engineering is the process of transforming raw data into useful features. Thesefeatures help machine learning(Introduction to Machine Learning) models learn patterns and make better predictions. It involves selecting, modifying, or creating new features fromexisting data. Without the right features, even powerful models may struggle. Good featureengineering improves accuracy, speeds up training, and enhances model performance.This process is crucial for predictive analytics. Raw data is often messy and unstructured.Models need clean, relevant features to make sense of the data. Feature engineering helpsextract valuable insights, making machine learning more effective. It bridges the gapbetween raw data and meaningful predictions.l.toLowerCase().replace(/\s+/g,"-")" id="cd998fa4-feba-42b1-8aa4-a406a6e63dd3" data-toc-id="cd998fa4-feba-42b1-8aa4-a406a6e63dd3">Importance of Feature Engineering in Machine LearningFeature engineering is essential for building high-performing machine learning models(Howto Build a Machine Learning Model?). Even the most advanced algorithms can fail if thefeatures are not relevant or well-structured. Poorly engineered features lead to inaccuratepredictions, slow training, and increased complexity. It helps in:● Improving model accuracy – Well-crafted features capture meaningful patterns indata, reducing errors and improving overall model precision. Without proper featureselection, models may overlook critical relationships or be misled by irrelevant data.● Reducing complexity and computational cost – Eliminating redundant orirrelevant features speeds up training(How Are AI Models Trained?) and reducesresource consumption. Simplified models are easier to deploy(How to Deploy aMachine Learning Model?), maintain, and scale.● Enhancing interpretability of results – When features are meaningful andwell-structured, it becomes easier to explain how a model makes decisions. This isespecially important in fields like healthcare and finance, where transparency iscrucial.● Addressing data imbalances and inconsistencies – Real-world data oftencontains missing values, outliers, and imbalanced classes. Feature engineeringtechniques, such as resampling or transformation, help ensure a more balanced andaccurate dataset.l.toLowerCase().replace(/\s+/g,"-")" id="708a927f-08bd-43de-ab03-067e32dae7a5" data-toc-id="708a927f-08bd-43de-ab03-067e32dae7a5">Steps Involved in Feature EngineeringFeature engineering follows a structured approach to convert raw data into meaningful inputfor machine learning models. Each step ensures the data is clean, relevant, and optimizedfor better predictions.l.toLowerCase().replace(/\s+/g,"-")" id="c861ed1c-10c8-4b5f-b1eb-df141b5897a5" data-toc-id="c861ed1c-10c8-4b5f-b1eb-df141b5897a5">1. Understanding the DataBefore modifying features, you need to analyze the dataset. This step involves examiningstructure, distribution, and relationships between variables. Identifying patterns andinconsistencies helps in making informed decisions. The key task is to visualize datadistributions and detect anomalies.l.toLowerCase().replace(/\s+/g,"-")" id="4030f7f3-037d-4c07-8d81-0e54be217755" data-toc-id="4030f7f3-037d-4c07-8d81-0e54be217755">2. Handling Missing DataReal-world datasets often contain missing values, which can lead to biased models. You canremove records with too many missing values or fill gaps using mean, median, or modeimputation. More advanced methods estimate missing values based on other data. The keytask is to choose the right imputation technique based on the data type.l.toLowerCase().replace(/\s+/g,"-")" id="7e19689b-7c27-4948-b723-ceeab6ce37d4" data-toc-id="7e19689b-7c27-4948-b723-ceeab6ce37d4">3. Feature SelectionNot all features contribute equally to model performance. Some may be redundant orirrelevant. Techniques like correlation analysis and recursive feature elimination (RFE) helpretain only the most useful features, reducing complexity and improving accuracy. The keytask is to remove irrelevant features to enhance model efficiency.l.toLowerCase().replace(/\s+/g,"-")" id="e8fc0f77-61e6-4dd2-8e95-8c3133d8b056" data-toc-id="e8fc0f77-61e6-4dd2-8e95-8c3133d8b056">4. Feature ExtractionFeature extraction creates new, informative features from existing data, especially whendealing with high-dimensional datasets. Examples include converting text into numerical embeddings(What are Embeddings in Machine Learning?) or extracting key features fromimages. The key task is to use domain knowledge to extract meaningful features.l.toLowerCase().replace(/\s+/g,"-")" id="1cede070-48cf-4620-8127-4d3156349d39" data-toc-id="1cede070-48cf-4620-8127-4d3156349d39">5. Feature TransformationDifferent machine learning models require specific data formats. Feature transformationensures compatibility through scaling (normalization, standardization) and encodingcategorical variables (one-hot encoding, label encoding). The key task is to standardizenumerical features for better model stability.l.toLowerCase().replace(/\s+/g,"-")" id="69fb0d01-396c-4522-9648-f244b3b38660" data-toc-id="69fb0d01-396c-4522-9648-f244b3b38660">6. Feature CreationSometimes, existing features are insufficient. New features can be created by combiningexisting ones, extracting time-based data, or applying domain expertise. For instance, "TotalRevenue" can be derived from "Units Sold" and "Price per Unit." The key task is to generatenew features that add predictive power.l.toLowerCase().replace(/\s+/g,"-")" id="d68a2603-0fa2-4c82-8118-0c5ae7870f24" data-toc-id="d68a2603-0fa2-4c82-8118-0c5ae7870f24">Processes Involved in Feature EngineeringFeature engineering is a crucial step in machine learning. It involves preparing and refiningdata to improve model performance. Here are the key processes involved:l.toLowerCase().replace(/\s+/g,"-")" id="ab873446-8a32-4821-b868-860b2ec04802" data-toc-id="ab873446-8a32-4821-b868-860b2ec04802">1. Creation of FeatureFeature creation is the process of generating new variables from existing data to improve amachine learning model’s performance. Sometimes, raw data does not provide enoughuseful information. By applying domain knowledge and logical transformations, new featurescan be derived to capture hidden patterns. For example, instead of using "date of birth"directly, converting it into "age" makes the data more meaningful for models. FeatureCreation types:● Mathematical Transformations – Applying functions like logarithm, square root, orpolynomial features.● Binning – Grouping continuous data into categories (e.g., age groups like child,adult, and senior).● Feature Interactions – Combining multiple features (e.g., price per square foot fromprice and area).● Aggregations – Summarizing data over time or categories (e.g., average monthlysales).● Encoding Categorical Data – Converting text-based categories into numericalvalues (e.g., one-hot encoding).Effective feature creation can drastically improve a model’s accuracy and efficiency. It helpsmachine learning algorithms detect patterns more easily, reducing the need for complexmodel structures. Additionally, well-designed features can minimize overfitting and improvegeneralization on unseen data.l.toLowerCase().replace(/\s+/g,"-")" id="bdf192a6-5348-4358-88b8-4433ef25786d" data-toc-id="bdf192a6-5348-4358-88b8-4433ef25786d">2. Transformation of FeatureFeature transformation modifies raw data into a format that is easier for machine learningmodels to interpret. Real-world data often contains inconsistencies, varying scales, orcomplex structures that can affect model performance. By transforming features, we ensurethat relationships between variables are better represented. For example, categorical datasuch as "low," "medium," and "high" can be converted into numerical values using one-hotencoding. Types of Feature Transformation are:● Normalization – Rescaling features to a standard range, such as 0 to 1.● Standardization – Converting data to have zero mean and unit variance.● Log Transformation – Applying a logarithmic function to reduce skewness in data.● One-Hot Encoding – Converting categorical variables into binary columns.● Polynomial Transformation – Creating new features by raising existing ones to apower.Proper feature transformation enhances the stability and efficiency of machine learningmodels. It ensures that all variables contribute equally, preventing certain features fromdominating due to scale differences. Moreover, transformations help in handling outliers,reducing noise, and improving the interpretability of results.l.toLowerCase().replace(/\s+/g,"-")" id="33698588-3d38-4444-be09-e4424d3b5b24" data-toc-id="33698588-3d38-4444-be09-e4424d3b5b24">3. Extraction of FeatureFeature extraction is the process of selecting and deriving relevant information from complexdatasets. Instead of using raw data, important features are extracted to reduce complexityand improve computational efficiency. This technique is especially useful when dealing withhigh-dimensional data, such as text, images, or sensor readings. For example, instead ofanalyzing entire text documents, extracting the "word count" or "keyword frequency" helpssummarize information efficiently. Types of Feature Extraction are:● Principal Component Analysis (PCA) – Reduces dimensionality while retainingimportant variance.● Word Embeddings (e.g., Word2Vec, TF-IDF) – Converts text into numericalrepresentations.● Edge Detection in Images – Extracts boundaries and important structures.● Fourier Transform – Converts signals into frequency components.● Histogram-based Features – Summarizes distributions in datasets like imageintensity or numerical data.Feature extraction enhances model performance by reducing redundancy and focusing oncritical data points. It helps in managing large datasets efficiently, minimizing computationalcosts while maintaining accuracy. Without proper feature extraction, machine learningmodels might struggle with unnecessary complexity, leading to slower training times andpotential overfitting.l.toLowerCase().replace(/\s+/g,"-")" id="360cfdd4-d3b5-4e27-b96c-ea339b78c3a8" data-toc-id="360cfdd4-d3b5-4e27-b96c-ea339b78c3a8">4. Selection of FeatureFeature selection is the process of identifying and retaining only the most relevant featureswhile removing redundant or irrelevant ones. Not all features contribute equally to a machinelearning model, and too many unnecessary variables can lead to overfitting, increasedcomputational cost, and slower processing. By selecting only the most useful features,models become more efficient and accurate. Feature selection ensures that the modelfocuses only on the most meaningful data points. Types of Feature Selection are:● Filter Methods – Uses statistical techniques like correlation analysis to removeirrelevant features.● Wrapper Methods – Evaluates subsets of features using model performance metrics(e.g., Recursive Feature Elimination).● Embedded Methods – Select features during model training, such as Lasso (L1regularization).● Mutual Information – Measures how much one feature contributes to predicting thetarget variable.● Variance Thresholding – Removes features with low variance that add little tomodel learning.Feature selection enhances model efficiency by reducing dimensionality, speeding uptraining, and improving interpretability. It prevents overfitting by eliminating noise andredundant data, allowing the model to generalize better on unseen data. Additionally,reducing the number of features lowers computational costs, making machine learningmodels more scalable and practical for real-world applications(Examples of MachineLearning).l.toLowerCase().replace(/\s+/g,"-")" id="7ca4d6f3-a6f0-454d-b349-690a95119ebc" data-toc-id="7ca4d6f3-a6f0-454d-b349-690a95119ebc">5. Scaling of FeatureFeature scaling is the process of adjusting numerical data values to a common scale,ensuring that all features contribute equally to a machine-learning model. Many algorithms,especially those based on distance calculations (e.g., k-NN, SVM, and neural networks),perform better when features are within a similar range. Without scaling, variables with largervalues may dominate the learning process, leading to biased results. Types of FeatureScaling are:● Min-Max Scaling – Rescales values to a fixed range, usually between 0 and 1.● Standardization (Z-score Normalization) – Centers data around a mean of 0 with astandard deviation of 1.● Robust Scaling – Uses median and interquartile range to handle outliers effectively.● Log Scaling – Applies a logarithmic transformation to reduce skewed distributions.Feature scaling improves model performance by ensuring fair comparisons betweenvariables, preventing large-value features from dominating. It enhances the stability ofgradient-based models, speeds up convergence in optimization algorithms, and improvesnumerical precision. Without scaling, models may struggle with inconsistent learning rates,resulting in slower training and suboptimal performance.l.toLowerCase().replace(/\s+/g,"-")" id="3b4319d3-4eb4-499d-bf45-4979ddcf925c" data-toc-id="3b4319d3-4eb4-499d-bf45-4979ddcf925c">Techniques Involved in Feature EngineeringFeature engineering involves various techniques that help transform raw data intomeaningful inputs for machine learning models. These methods enhance model accuracy,improve interpretability, and handle complex data relationships. Below are some keytechniques used in feature engineering:● Binning – Converts continuous numerical values into discrete categories or bins.This helps in handling outliers, reducing model complexity, and improvinginterpretability. For example, instead of using exact ages, we can categorize theminto groups like "child (0-12)," "teen (13-19)," "adult (20-59)," and "senior (60+)."Binning is commonly used in decision trees and statistical analysis to simplifypatterns.● Polynomial Features – Creates new features by applying polynomialtransformations to existing numerical variables. This helps capture non-linearrelationships in the data. For example, adding squared or cubic terms (e.g., x2x^2x2,x3x^3x3) can improve regression models by allowing them to fit curves instead of juststraight lines. Polynomial features are useful in models like polynomial regressionand kernel methods in SVMs.● Log Transformation – Applies a logarithmic function to numerical data to stabilizevariance and reduce skewness in distributions. This is particularly helpful for datasetswith highly skewed values, such as income or sales data. For example, if a datasethas extreme differences in values (like some values in the thousands and others insingle digits), log transformation can bring them closer together, making the modelmore robust and improving linearity.● Mean Encoding – Replaces categorical values with their corresponding mean targetvalue. This technique is useful when dealing with categorical data that has arelationship with the target variable. For example, in a dataset predicting houseprices, if "neighborhood" is a feature, we can replace it with the average house priceof each neighborhood. Mean encoding captures hidden relationships betweencategorical variables and the target but must be used carefully to avoid data leakage.● Text Feature Extraction – Converts text data into numerical representations usingtechniques like TF-IDF (Term Frequency-Inverse Document Frequency) and wordembeddings (Word2Vec, GloVe, BERT). TF-IDF gives importance to words based ontheir frequency across documents, helping identify keywords. Word embeddingsrepresent words in vector space, capturing their meanings and relationships. Thesetechniques are crucial for natural language processing (NLP) tasks like sentimentanalysis, text classification, and chatbot development.● Time-Based Feature Engineering – Extracts meaningful insights from time-relateddata, such as identifying trends, seasonality, or periodic patterns. For example, insales data, creating features like "day of the week," "is weekend," or "holiday season"can improve forecasting models. Additionally, calculating time differences, rollingaverages, and cumulative sums can enhance time-series models like ARIMA andLSTMs.By applying these feature engineering techniques, data scientists can enhance modelperformance, reduce noise, and improve prediction accuracy.l.toLowerCase().replace(/\s+/g,"-")" id="84548564-e34e-431c-af10-affec1a0e2e7" data-toc-id="84548564-e34e-431c-af10-affec1a0e2e7">Tools Involved in Feature EngineeringFeature engineering relies on various tools and libraries(Python Machine Learning Libraries)that help manipulate, transform, and extract meaningful insights from data. These toolssimplify complex data processes, improve model performance, and enhance efficiency.Below are some essential tools used in feature engineering:1. Pandas – A powerful Python library for data manipulation and preprocessing. Itprovides data structures like DataFrames and Series, enabling easy data cleaning,handling missing values, and feature transformations.2. NumPy – A fundamental library for numerical computing and array operations. Itoffers efficient handling of large datasets, mathematical functions, and vectorizedoperations, making it ideal for feature creation and transformation.3. Scikit-learn – A widely used machine learning library that provides tools for featuretransformation, selection, and preprocessing. It includes methods like scaling,encoding, PCA, and recursive feature elimination (RFE) to optimize model inputs.4. Featuretools – An automated feature engineering library that helps generate newfeatures from relational datasets. It is particularly useful for time-series data andpredictive modeling, reducing manual effort in feature creation.5. TensorFlow & PyTorch – Deep learning frameworks that include built-in functionsfor feature extraction from images, text, and structured data. They enable thetransformation of raw inputs into meaningful features for neural networks.6. SQL & BigQuery – Essential for handling large datasets, these tools help extract,transform, and aggregate data efficiently. SQL queries assist in filtering, grouping,and joining datasets, while BigQuery is optimized for cloud-based big dataprocessing.l.toLowerCase().replace(/\s+/g,"-")" id="746ea980-9291-4ab6-9fb9-9961baa67007" data-toc-id="746ea980-9291-4ab6-9fb9-9961baa67007">Best Practices for Feature EngineeringEffective feature engineering requires a strategic approach to ensure high-quality data inputsfor machine learning(Introduction to Machine Learning with Python) models. Following bestpractices helps improve model accuracy, efficiency, and interpretability.Understand the Domain – Leverage domain expertise to create meaningful features.Understanding the data's context allows for better feature selection and transformation,leading to more relevant insights.Use Feature Selection Techniques – Remove irrelevant or redundant features to avoidunderfitting and overfitting. Techniques like recursive feature elimination (RFE), mutualinformation, and statistical tests help retain only the most valuable features.Test Different Feature Combinations – Experiment with various feature sets to determinewhich combinations yield the best model performance. Some features may work bettertogether, while others may introduce unnecessary complexity.Ensure Data Consistency – Maintain uniformity in data types, formats, and scales.Standardizing and normalizing numerical values prevent biases caused by different measurement units, ensuring fair model comparisons.Document the Process – Keep track of feature transformations, selections, andengineering steps for reproducibility and debugging. Well-documented processes help inunderstanding model behavior and making improvements over time.l.toLowerCase().replace(/\s+/g,"-")" id="87566a57-e60a-4f6f-8577-3626119a83d1" data-toc-id="87566a57-e60a-4f6f-8577-3626119a83d1">ConclusionFeature engineering plays a crucial role in machine learning. It directly affects modelaccuracy and interpretability. Well-designed features help models learn patterns moreeffectively, leading to better predictions.By using the right techniques and tools, data scientists can refine raw data into valuableinsights. Transforming, selecting, and scaling features improve efficiency while reducingcomplexity. Testing different feature combinations further enhances performance.A strong feature set often separates average models from high-performing ones. Carefulengineering ensures that models generalize well to new data. Ultimately, feature engineeringis a key step in building reliable and efficient machine learning solutions.l.toLowerCase().replace(/\s+/g,"-")" id="10be29da-44d6-4631-b8aa-d45db1261751" data-toc-id="10be29da-44d6-4631-b8aa-d45db1261751">FAQ’sl.toLowerCase().replace(/\s+/g,"-")" id="64eb3e71-dc3b-4f5f-9130-412543b17868" data-toc-id="64eb3e71-dc3b-4f5f-9130-412543b17868">Q1: Is feature engineering necessary for all machine learning models?Yes, feature engineering is crucial for improving model performance, though some models,like deep learning, can automatically extract features.l.toLowerCase().replace(/\s+/g,"-")" id="5fa9edd1-53aa-4662-b2fb-e78a65656511" data-toc-id="5fa9edd1-53aa-4662-b2fb-e78a65656511">Q2: How do I know which features to keep?Feature selection techniques like correlation analysis, mutual information, and recursivefeature elimination help identify important features.l.toLowerCase().replace(/\s+/g,"-")" id="a3444ceb-f126-4c0d-b02d-8e276fca5196" data-toc-id="a3444ceb-f126-4c0d-b02d-8e276fca5196">Q3: Can feature engineering be automated?Yes, automated tools like Featuretools and AutoML frameworks help streamline the featureengineering process.l.toLowerCase().replace(/\s+/g,"-")" id="83dcf1a0-9b32-47c9-a795-018731a4c168" data-toc-id="83dcf1a0-9b32-47c9-a795-018731a4c168">Q4: What is the difference between feature selection and feature extraction?Feature selection involves choosing existing relevant features, while feature extractioncreates new features from existing data.