Understanding SVM, its Type, Applications and How to use with Python

Blog Contents:

• Introduction to SVM
• What is SVM?
• Types of SVM
• Application of SVM
• How to SVM in Machine learning model using Python

Introduction to SVM :

SVM (Support Vector Machine) is a powerful machine learning tool that is used for classification and regression.

What is SVM?

It is a supervised Machine learning algorithm that is used for classification and regression you can even detect outliers with it. It is very for classification problems when your data is complex -small and medium-sized. The purpose of SVM is to draw lines or decision boundaries that can separate n-dimensional space this is very useful for classification. The best plane or decision boundary which separate the data most accurately is called Hyperplane.

Type of SVM:

There are two types of SVM

• Linear SVM: Those SVM models in which data can be classified using a straight line are called Linear SVM.
  
  

  

  Image 1

  
  
• Non-Linear SVM: Those SVM models in which data can be classified using a Non-Linear line are called Non-Linear SVM.

Image 2

Application of SVM:

There are many applications of SVM model like:

• Classification of Images
• Face detection
• Bioinformatics
• Handwriting Recognition and much more

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Python Code With SVM

import numpy as np

import matplotlib.pyplot as plt

from sklearn import svm, datasets

from ipywidgets import interactive

# import some data to play with

iris = datasets.load_iris()

X = iris.data[:, :2]  # we only take the first two features. We could

                      # avoid this ugly slicing by using a two-dim dataset

y = iris.target

h = .02  # step size in the mesh

# we create an instance of SVM and fit out data. We do not scale our

# data since we want to plot the support vectors

def f(C):

      # SVM regularization parameter

    svc = svm.SVC(kernel='linear', C=C).fit(X, y)

    rbf_svc = svm.SVC(kernel='rbf', gamma=0.7, C=C).fit(X, y)

    poly_svc = svm.SVC(kernel='poly', degree=3, C=C).fit(X, y)

    lin_svc = svm.LinearSVC(C=C).fit(X, y)

    # create a mesh to plot in

    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1

    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1

    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),

                         np.arange(y_min, y_max, h))

    # title for the plots

    titles = ['SVC with linear kernel',

              'LinearSVC (linear kernel)',

              'SVC with RBF kernel',

              'SVC with polynomial (degree 3) kernel']

    for i, clf in enumerate((svc, lin_svc, rbf_svc, poly_svc)):

        # Plot the decision boundary. For that, we will assign a color to each

        # point in the mesh [x_min, x_max]x[y_min, y_max].

        plt.subplot(2, 2, i + 1)

        plt.subplots_adjust(wspace=0.4, hspace=0.4)

        Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])

        # Put the result into a color plot

        Z = Z.reshape(xx.shape)

        plt.contourf(xx, yy, Z, cmap=plt.cm.coolwarm, alpha=0.8)

        # Plot also the training points

        plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.coolwarm)

        plt.xlabel('Sepal length')

        plt.ylabel('Sepal width')

        plt.xlim(xx.min(), xx.max())

        plt.ylim(yy.min(), yy.max())

        plt.xticks(())

        plt.yticks(())

        plt.title(titles[i])

    plt.show()

interactive_plot = interactive(f,C = (1,100))

interactive_plot

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