This tutorial demonstrates the implementation of Support Vector Machine (SVM) from Scikit Learn library.
###Importing Libraries
from sklearn import datasets
from sklearn import metrics
from sklearn import preprocessing
from sklearn import svm
from sklearn.model_selection import train_test_split as tts
###Importing Dataset
iris = datasets.load_iris()
data = pd.DataFrame({"sl":iris.data[:,0], "sw":iris.data[:,1], "pl":iris.data[:,2], "pw":iris.data[:,3], 'species': iris.target})
###Splitting train/test data
X=data[['sl','sw','pl','pw']]
y=data["species"]
X_tr, X_ts, y_tr, y_ts = tts(X,y, test_size=30/100, random_state=None)
###Creating SVM Classifier Model
clf = svm.SVC(kernel='rbf') # Linear Kernel
###Training the Model
clf.fit(X_tr, y_tr)
###Making Predictions
y_pr = clf.predict(X_ts)
###Evaluating Prediction Accuracy
print("Acc %:",metrics.accuracy_score(y_ts, y_pr)*100)
###Making Prediction with Foreign Data
print(clf.predict([[1,1,0.5,6]]))
This tutorial aims to provide a simple, clear and reusable Support Vector Machine implementation, so that, seasoned visitors can just take a look at it, comprehend what’s going on and copy/reproduce it for their own use cases without losing too much time.
If you’d like to see a step by step explanation of this algorithm you can check out this tutorial.
You can also see its history, read about the optimization parameters and find more examples in the main Support Vector Machine page.
