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

 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

My Github For Other Project : Link [I am looking for a fresher Internship or entry-level machine learning/data science job I have learned machine learning myself,I am a mechanical engineer currently doing freelance work in 3D animation. if anybody can help me please comment I will be thankful.CONSTRUCTIVE CRITICISM HIGHLY APPRECIATE]

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())

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

Other Blog You might also like:

Post a Comment