the above series of and are the predicted values for the tumor classes malignant and benign nowby comparing the two arrays namely test_labels and predswe can find out the accuracy of our model we are going to use the accuracy_score(function to determine the accuracy consider the following commandfrom sklearn metrics import accuracy_score print(accuracy_score(test_labels,preds) the result shows that naivebayes classifier is accurate that was machine learning classifier based on the naive bayse gaussian model support vector machines (svmbasicallysupport vector machine (svmis supervised machine learning algorithm that can be used for both regression and classification the main concept of svm is to plot each data item as point in -dimensional space with the value of each feature being the value of particular coordinate here would be the features we would have following is simple graphical representation to understand the concept of svmin the above diagramwe have two features hencewe first need to plot these two variables in two dimensional space where each point has two co-ordinatescalled support vectors the line splits the data into two different classified groups this line would be the classifier herewe are going to build an svm classifier by using scikit-learn and iris dataset scikitlearn library has the sklearn svm module and provides sklearn svm svc for classification the svm classifier to predict the class of the iris plant based on features are shown below