Support Vector Machines | Machine Learning | Python

Introduction

In this lab, we will learn how to use Support Vector Machines (SVM) to classify a sample using a custom kernel. We will use Python's scikit-learn library to perform SVM classification with a custom kernel. SVM is a popular machine learning algorithm used for classification, regression, and outlier detection. SVM works by creating a boundary or a line (hyperplane) that separates the data into classes.

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Skills Graph

%%%%{init: {'theme':'neutral'}}%%%% flowchart RL sklearn(("`Sklearn`")) -.-> sklearn/ModelSelectionandEvaluationGroup(["`Model Selection and Evaluation`"]) ml(("`Machine Learning`")) -.-> ml/FrameworkandSoftwareGroup(["`Framework and Software`"]) sklearn/ModelSelectionandEvaluationGroup -.-> sklearn/inspection("`Inspection`") ml/FrameworkandSoftwareGroup -.-> ml/sklearn("`scikit-learn`") subgraph Lab Skills sklearn/inspection -.-> lab-49097{{"`SVM Classification Using Custom Kernel`"}} ml/sklearn -.-> lab-49097{{"`SVM Classification Using Custom Kernel`"}} end

Import Libraries

In this step, we will import the necessary libraries for this lab. We will be using numpy, matplotlib, scikit-learn, and DecisionBoundaryDisplay.

import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets
from sklearn.inspection import DecisionBoundaryDisplay

Load Data

In this step, we will load the iris dataset using scikit-learn's datasets module. We will select the first two features of the dataset and assign it to the variable X. We will also assign the target variable to Y.

iris = datasets.load_iris()
X = iris.data[:, :2]
Y = iris.target

Create Custom Kernel

In this step, we will create a custom kernel. The custom kernel will be a dot product of two matrices. We will create a matrix M with the values [[2, 0], [0, 1.0]]. We will then multiply the matrices X and Y with M and take their dot product.

def my_kernel(X, Y):
    """
    We create a custom kernel:

                 (2  0)
    k(X, Y) = X  (    ) Y.T
                 (0  1)
    """
    M = np.array([[2, 0], [0, 1.0]])
    return np.dot(np.dot(X, M), Y.T)

Create SVM Classifier

In this step, we will create an instance of SVM classifier and fit our data. We will use the custom kernel created in the previous step.

clf = svm.SVC(kernel=my_kernel)
clf.fit(X, Y)

Plot Decision Boundary

In this step, we will plot the decision surface and the support vectors. We will use the DecisionBoundaryDisplay module from scikit-learn's inspection module to plot the decision boundary. We will also scatter plot the training points.

ax = plt.gca()
DecisionBoundaryDisplay.from_estimator(
    clf,
    X,
    cmap=plt.cm.Paired,
    ax=ax,
    response_method="predict",
    plot_method="pcolormesh",
    shading="auto",
)

plt.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired, edgecolors="k")
plt.title("3-Class classification using Support Vector Machine with custom kernel")
plt.axis("tight")
plt.show()

Summary

In this lab, we learned how to use Support Vector Machines (SVM) to classify a sample using a custom kernel. We used scikit-learn library to perform SVM classification with a custom kernel. We loaded the iris dataset, created a custom kernel, created an instance of SVM classifier and fit our data, and plotted the decision boundary and the support vectors. SVM is a popular machine learning algorithm used for classification, regression, and outlier detection.

SVM Classification Using Custom Kernel