Machine Learning is a very vast field with a multitude of algorithms and methodologies for model creation. One such algorithm that is extensively used is the Support Vector Machine (SVM). Support Vector Machines are a set of supervised learning methods designed for classification and regression analysis. In simple words, SVM is a discriminating classifier that is formally designed by a separative hyperplane.
Support Vector Machine algorithms are designed for binary classification. These algorithms intend to find a hyperplane in an N-dimensional space, where N is the number of features, to distinctly classify the data points. The SVM will choose the hyperplane that maximizes the margin to the nearest data points of any class. These points are known as support vectors.
Here, we'll show a simple example of using SVM on the iris dataset using python's Scikit-Learn library.
# Importing required libraries from sklearn import datasets from sklearn.model_selection import train_test_split from sklearn import svm from sklearn import metrics # Loading the iris dataset iris = datasets.load_iris() # Splitting dataset into train and test X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.3,random_state=2) # Defining the model svm_classifier = svm.SVC(kernel='linear') # Training the model svm_classifier.fit(X_train, y_train) # Predicting the result predictions = svm_classifier.predict(X_test) # Evaluating the result print(f"Accuracy: {metrics.accuracy_score(y_test, predictions)}")
In the above python code, we used the linear kernel to create the svm classifier model, trained it on the training dataset, predicted the results for the test data, and finally, evaluated the accuracy of the model.
Support Vector Machines are powerful tools for classification problems. They are effective in high-dimensional spaces and behave superbly when the number of dimensions is greater than the number of samples. However, they are not suitable for large data sets because the required training time is higher. SVMs also do not directly provide probability estimates. These are calculated using expensive five-fold cross-validation. Understanding the concept and functionality of SVMs is essential in the realm of Machine Learning.