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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
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Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
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Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

What is Feature Extraction and Feature Extraction Techniques

D
Dishaa Last Updated : 28 Nov, 2024
7 min read
Feature Extraction and Feature Extraction Techniques

Table of contents

What is Feature Extraction?

 Feature extraction is the process of identifying and selecting the most important information or characteristics from a data set. It’s like distilling the essential elements, helping to simplify and highlight the key aspects while filtering out less significant details. It’s a way of focusing on what truly matters in the data.

Why feature Extraction is essential?

Feature extraction is important because it makes complicated information simpler. In things like computer learning, it helps find the most crucial patterns or details, making computers better at predicting or deciding things by focusing on what matters in the data.

Common Feature Extraction Techniques

1. The need for Dimensionality Reduction

In real-world machine learning problems, there are often too many factors (features) on the basis of which the final prediction is done. The higher the number of features, the harder it gets to visualize the training set and then work on it. Sometimes, many of these features are correlated or redundant. This is where dimensionality reduction algorithms come into play.

2. What is Dimensionality reduction

Dimensionality reduction is the process of reducing the number of random features under consideration, by obtaining a set of principal or important features.

Dimensionality reduction can be done in 2 ways:

a. Feature Selection: By only keeping the most relevant variables from the original dataset

  1. Correlation
  2. Forward Selection
  3. Backward Elimination
  4. Select K Best
  5. Missing value Ratio

Please refer to this link for more information on the Feature Selection technique

b. Feature Extraction: By finding a smaller set of new variables, each being a combination of the input variables, containing basically the same information as the input variables.

  1. PCA(Principal Component Analysis)
  2. LDA(Linear Discriminant Analysis)
Feature Extraction DR

In this article, we will mainly focus on the Feature Extraction technique with its implementation in Python.

The feature Extraction technique gives us new features which are a linear combination of the existing features. The new set of features will have different values as compared to the original feature values. The main aim is that fewer features will be required to capture the same information. We might think that choosing fewer features might lead to underfitting but in the case of the Feature Extraction technique, the extra data is generally noise.

The feature extraction in machine learning technique provides us with new features, forming a linear combination of the existing ones. This results in a new set of features with values different from the original ones. The primary objective is to require fewer features to capture the same information. While one might assume that opting for fewer features could lead to underfitting, in the case of the feature extraction technique, the additional data is typically considered noise.

3. PCA(Principal Component Analysis)

In simple words, PCA is a method of obtaining important variables (in form of components) from a large set of variables available in a data set. It tends to find the direction of maximum variation (spread) in data. PCA is more useful when dealing with 3 or higher-dimensional data.

PCA can be used for anomaly detection and outlier detection because they will not be part of the data as it would be considered noise by PCA.Building PCA from scratch:

  1. Standardize the data (X_std)
  2. Calculate the Covariance-matrix
  3. Calculate the Eigenvector & Eigenvalues for the Covariance-matrix.
  4. Arrange all Eigenvalues in decreasing order.
  5. Normalize the sorted Eigenvalues.
  6. Horizontally stack the Normalized_ Eigenvalues =W_matrix
  7. X_PCA=X_std .dot (W_matrix)
PCA, feature extraction

We can infer from the above figure that from the first 6 Principal Components we are able to capture 80% of the data. This shows us the Power of PCA that with only using 6 features we able to capture most of the data.

A principal component is a normalized linear combination of the original features in a data set.

The first principal component(PC1) will always be in the direction of maximum variation and then the other PC’s follow.

We need to note that all the PC’s will be perpendicular to each other. The main intention behind this is that no information present in PC1 will be present in PC2 when they are perpendicular to each other.

dimension,feature extraction in machine learning
PC’s are Perpendicular to each other

Implementation of PCA using Python

For this, I have used the Wine dataset. In this part, I have implemented the PCA along with Logistic regression followed by Hyperparameter Tuning.

a. First we standardize the data and apply PCA. Then I have plotted the result to check the separability.

apply PCA feature extraction

b. Then I have applied Logistics Regression and plotted with the help of the Decision boundary for the train and test data.

Feature Extraction Decision Boundary for Train data

Decision Boundary for Train data

Feature Extraction Decision Boundary for Test data

Decision Boundary for Test data

c. Finally I had applied Hyperparameter Tuning with Pipeline to find the PC’s which have the best test score.

Hyperparameter Tuning

Hyperparameter Tuning

4. Kernel PCA

We know that PCA performs linear operations to create new features. PCA fails when the data is non-linear and is not able to create the hyperplane.

This is where Kernel PCA comes to our rescue. It is similar to SVM in the way that it implements Kernel–Trick to convert the non-linear data into a higher dimension where it is separable.

Kernel PCA

Kernel PCA

In the above figure we can see that PCA is not able to separate non-linear data but with the help of Kernel -PCA it is able to generate class-separability.

5. Disadvantages of PCA:

PCA does not guarantee class separability which is why it should be avoided as much as possible which is why it is an unsupervised algorithm. In other words, PCA does not know whether the problem which we are solving is a regression or classification task. That is why we have to be very careful while using PCA.

LDA v/s PCA,feature extraction in machine learning

LDA v/s PCA

6. LDA

Though PCA is a very useful technique to extract only the important features but should be avoided for supervised algorithms as it completely hampers the data.

If we still wish to go for Feature Extraction Technique then we should go for LDA instead.

LDA,Supervised learning vs unsupervised learning

The main difference between LDA and PCA is:

1. LDA is supervised PCA is unsupervised.

2. LDA =Describes the direction of maximum separability in data.PCA=Describes the direction of maximum variance in data.

3. LDA requires class label information unlike PCA to perform fit ().

LDA works in a similar manner as PCA but the only difference is that LDA requires class label information, unlike PCA.

Implementation of LDA using Python

a. I have first standardized the data and applied LDA.

b. Then I have used a linear model like Logistic Regression to fit the data. Then plotted the Decision Boundary for better class separability understanding

LDA with Logistic Regression

LDA with Logistic Regression

Note: We can see that LDA is a linear model and passing the output of one linear model to another does no good. It is better to try passing the output of the linear model to a nonlinear model.

From the above figure, we were able to achieve an accuracy of 100% for the train data and  98% for the test data.

c. Thus, this time we have used a nonlinear model (SVM) to prove the above.

Feature Extraction LDA with SVM

LDA with SVM

From the above figure, we were able to achieve an accuracy of 100% for both the test and train data.

Thus we can see that passing linear input to a nonlinear model is more beneficial instead.

Conclusion

Feature Extraction, essential for data analysis, involves techniques like Principal Component Analysis (PCA) for Dimensionality Reduction. By reducing complexity, it enhances efficiency, making it a crucial tool in extracting meaningful patterns from data for better insights and decision-making.

For the Code, implementation refer to my GitHub link: Dimensionality Reduction Code Implementation in Python

Hope you find this article informative. I’m looking forward to hearing your views and ideas in the comments section.

Frequently Asked Questions

Q1.Which algorithm is best for feature extraction?

The best algorithm depends on the task. Popular ones are PCA, t-SNE, and Autoencoders. Choose based on specific needs.

Q2. How feature extraction is different in ML and DL?

In  Machine Learning (ML), people manually pick and create features, following set rules. It involves choosing a limited number of features based on human expertise.

In Deep Learning (DL), the computer figures out which features are important by itself. DL handles a large number of raw features, and the model learns to extract and represent features, reducing the need for human-defined rules

D
Dishaa

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Aftab Alam
Aftab Alam

Awesome....! very helpful thank you so much Sir.

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