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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
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
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

Support Vector Machine with Kernels and Python Iterators

K
Kavish Last Updated : 08 Jun, 2022
6 min read

This article was published as a part of the Data Science Blogathon.

Introduction

This article will cover the concepts of Iterators, Generators, and Decorators in Python. Later, we will discuss the Maximal-Margin Classifier and Soft Margin Classifier for Support Vector Machine. At last, we will learn about some SVM Kernels, such as Linear, Polynomial, and RBF. A data scientist often uses these advanced Python methods and SVM algorithms in his daily work. Understanding these concepts is rather confusing than it is difficult. As a Python newbie, anyone can get confused with Iterator, generator, and Decorators. To clear the confusion and better understand, we will discuss these concepts with their implementation in Python.

The article will cover the following concepts:
  • Iterators
  • Generator
  • Decorator
  • SVM

Iterators

Iterators are Objects or collections through which one can iterate element-wise. Examples of such iterators are List, Tuples, Sets, and Dictionaries.

Python has the ‘iter()’ keyword to create an Iterator and the ‘next()’ keyword to access the elements.

Now the question arises, “why do we create an iterator when we can iterate into a list, set, or dictionary directly?”

Iterators enable programmers to perform lazy operations. We can access the next element without traversing through previous elements. Hence, We need iterators to save CPU time and memory.

In-built iterator classes in Python, enumerate, reversed, zip, map, and filter.

Iterators | Support Vector Machine

 

Generators

Generators are expressions of function that create an iterator. A function returns a value while a generator returns an iterator. To create a generator ‘yield’ keyword is used instead of ‘return.’

We can use a generator object like an iterator, using the ‘next’ keyword or iterating through it using the ‘for’ loop.

Let’s see its implementation in Python:

Generators | Support Vector Machine

The ‘yield’ keyword pauses the function in its defined state while the ‘return’ statement terminates that function.

A generator expression is different from a generator function. Such expressions get used as function arguments and created like list comprehensions.

Generator expressions use lazy evaluation and create one element once, while list comprehension computes an entire list.

Thus, generator expressions are much more memory efficient.

Decorators

As the name suggests, Decorators in Python function like objects used to decorate a function. The decorator takes a function as an argument and then applies its defined functionality to that function and returns the decorated function as its output.

Decorators get used for controlling or modifying the behaviour of function or class. We call a decorator before defining the function/class. There are two different kinds of decorators in Python:

  • Class Decorators
  • Function Decorators

Let’s implement the function decorator in Python

Decorators | Support Vector Machine

We can also define multiple decorators:

Decorators 2 | Support Vector Machine

 

Support Vector Machine (SVM)

SVM is one of the most popular Machine Learning algorithms. This supervised algorithm gets used for both, Classification and Regression problems. Here, we will discuss the high-level concepts behind the SVM algorithm.

Maximal-Margin Classifier

This classifier is more of a hypothetical type that best explains the working of the SVM classifier.

This classifier creates a hyperplane and then classifies the inputs to either side of that hyperplane. A line that splits the input feature space is the hyperplane. The more the margin between the hyperplane and the prediction, the better it performs.

Maximal Margin Classifiers

Let’s assume a dataset(x) with two input variables that form a two-dimensional space. And the target variable has binary labels, 0 and 1. Now, we can visualize the hyperplane as a line in this two-dimensional space. Let’s assume that the hyperplane separates all input points. For example:

B0+(W1*X1)+(W2*X2) = 0

W1 and W2 determine the line slope, and B0 is the intercept. X1 and X2 are two input feature vectors.

This line assists with classifying the input values using the equation.

  • If the output of the equation is more than zero, the input lies above the line.
  • If the equation returns a value less than zero, the point will lie below the line.
  • An output value close to zero indicates that the input is close to the hyperplane. That point is difficult to classify.
  • A large magnitude value may have more probability of correct classification.

The margin is the perpendicular distance between the line and the closest data point. The best line gets decided using an optimization procedure the optimal weights and bias. The best line that separates the two classes with the maximum margin is the Maximal-margin hyperplane.

Points that help in constructing the classifier and are relevant in determining the hyperplane are support vectors.

Soft Margin Classifier

The data we use in real life is often messy and can not be separated by a hyperplane. Therefore, the criteria for finding a hyperplane were relaxed, and a soft margin classifier was introduced. This softness enables the data points to violate the hyperplane line.

Soft-Margin Classifier

The soft margin classifier has additional parameters to provide the margin softness room in each dimension. This addition has increased the model complexity since it has more parameters for the model to fit the data to match such complexity.

One of such parameters is C which determines the allowed softness magnitude across all dimensions. This coefficient determines the flexibility range of margin violation. If the value of C=0, the flexibility is also none, and it is the same as Maximal-Margin. It indicates that the violations of hyperplane margin are directly proportional to C.

The training data points or examples that lie under the margin during the learning process of hyperplane are support vectors. These vectors also affect the position of the hyperplane, and C affects these points as well.

  • The small value of C makes the algorithm more sensitive to the training data, leading to overfitting (High Variance and Low Bias).
  • The large C reduces the sensitivity of the algorithm to the training data, leading to underfitting (Low Variance and High Bias).

Thus, the SVM algorithm determines the hyperplane to separate the classes. SVM uses Kernels to increase the complexity of the model to fit the more complex data.

Support Vector Machine Kernels

A kernel is nothing but the dot product. There are several kernels in SVM. Mostly, we use Linear, RBF, and Polynomial Kernels.

Linear Kernel

For binary classification, we use linear kernel or linear svm.

K(x, xi) = sum(x*xi)

For more complex classes or multi-class classification, we use more complex kernels.

Polynomial Kernel

The polynomial kernel uses polynomial expression instead dot product.

K(x, xi) = 1+ sum(x*xi)^d

d is the polynomial degree, a hyperparameter, and if d =1, it is the same as the linear svm.

Radial Basis Function (RBF)

It is a more complex kernel than the Polynomial and Gaussian Kernels.

K(x,xi) = exp(-gamma*sum(x*xi^2))

The hyperparameter gamma lies between 0 and 1, with the default value being 0.1. This local kernel can create complex segments inside the feature space.

Conclusion

In this article, we have learned some essential advanced Python concepts such as Iterators, Generators, and Decorators. Now let’s discuss some takeaways from this article:

  • Iterators are Python objects that get used for creating more efficient collections of elements.
  • Generators are Python functions that use the yield keyword and return an iterator.
  • Generator expressions are more memory efficient since they insert one element once.
  • Decorators are Python objects which get used for controlling or modifying function behaviour in Python.
  • Support Vector Machine uses hyperplane to separate class instances using Maximal-margin and Soft Margin Classifier.
  • Support Vector Machine kernels help with defining hyperplanes for more complex input space and multi-class classification.

Thank you for reading this article. For any feedback and query, feel free to reach me on Linkedin.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.

K
Kavish

I am a freelance Data Science professional with immense passion to solve Data driven problem. I have almost one year of industry experience. I love to write, explore new use cases of data science, and travel. I am open to a collaboration to learn, teach and grow more.

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