Using SVM Classifier

Support vector Machine (SVM) is one of most famous machine learning tool for classification problem. This is supervised learning technique .Read More

SVM Margin

 we are going to see how to use SVM classifier in python.

Our Demonstration uses digit dataset . This dataset uses 64 feature vector to identify handwritten digit [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] . It means this 64 feature extracted from Handwritten digit used to classify handwritten digit in 9 classes .

So Again we gonna use skilearn Python Package .



Lets see some about Some of Parameter of our classifier that is used to customize our classification .
We discuss only few most useful parameters .

• kernel : Used to convert low dimensional feature space to high dimensional space in order to make it separable. example : rbf,poly,leaner.
• C : Controls trade off between smooth decision boundary and classify training point correctly. High value leads to over-fitting that is not good for generalisation.
• gamma : Defines how far influence of a single training example reach.(high value of gamma leads to over fitting) so for generalise classification we need low value of gamma .

Others Parameters are : cache_size, class_weight, coef0, decision_function_shape, max_iter, probability, random_state, shrinking, tol, verbose .

What is Over-fitting 

From this figure you can see the Difference . But real question is which situation is good. And Answer for this problem is figure1 . Overfitting Situation is not good but it looks like that it will gives better result . but when you will apply this classifier for general new data you will see that performance of over fitting is less then proper fitted hyperplain .

Hyperplain : Line or plain in n dimensional space that is used as boundary of classification  .

SVM Classifier [Python] [Github link]

We Use rbf kernel.

1. __auther__='www.codecops.in'
2. #SVM Classifier
3. import sklearn.datasets as data
4. from sklearn.svm import SVC
5. import numpy as np
7. # Get digits Dataset****
8. data=data.load_digits()
9. X=data.data #feature vector
10. Y=data.target #Label Vector
11. # **************************
13. if __name__=='__main__':
14.     # clsf = classifier
15.     clsf=SVC(kernel='rbf',gamma=0.001,C=0.1) #SVM Classier
16.     # There are other argument like
17.     # [[[C, cache_size, class_weight, coef0,
18.     # decision_function_shape, gamma, kernel,
19.     # max_iter, probability, random_state,shrinking,
20.     # tol,verbose]]]
21.     # you can pass in order to custmize your classifier
23.     # Trainig Classifier  ***
24.     clsf.fit(X, Y)
26.     #Now predict values for given classifier
27.     prediction = clsf.predict(X)
29.     #
30.     print 'printing data for few classification'
31.     for i in [4,50,200,300,600,700,900,1100,1500,1600,1700,344,1123]:
32.         print 'Feature : ',X[i],'\t Real Digit :',Y[i],'\tPredicted Digit',prediction[i]
33.         print '********************************'
34.     print '\n\n\n'
36.     # Print Accuracy Test
37.     from sklearn.metrics import accuracy_score
38.     print 'Accuracy Check ',accuracy_score(prediction,Y)*100,'%  Wow _/\_ that is GOOD :)'

Output :

Result with approximately 98% accuracy