from sklearn.datasets import load_iris from sklearn import svm from sklearn.model_selection import train_test_split import doctest # different functions implementing different types of SVM's def NuSVC(train_x, train_y): svc_NuSVC = svm.NuSVC() svc_NuSVC.fit(train_x, train_y) return svc_NuSVC def Linearsvc(train_x, train_y): svc_linear = svm.LinearSVC() svc_linear.fit(train_x, train_y) return svc_linear def SVC(train_x, train_y): # svm.SVC(C=1.0, kernel='rbf', degree=3, gamma=0.0, coef0=0.0, shrinking=True, probability=False,tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, random_state=None) # various parameters like "kernal","gamma","C" can effectively tuned for a given machine learning model. SVC = svm.SVC(gamma="auto") SVC.fit(train_x, train_y) return SVC def test(X_new): """ 3 test cases to be passed an array containing the sepal length (cm), sepal width (cm),petal length (cm),petal width (cm) based on which the target name will be predicted >>> test([1,2,1,4]) 'virginica' >>> test([5, 2, 4, 1]) 'versicolor' >>> test([6,3,4,1]) 'versicolor' """ iris = load_iris() # splitting the dataset to test and train train_x, test_x, train_y, test_y = train_test_split( iris["data"], iris["target"], random_state=4 ) # any of the 3 types of SVM can be used # current_model=SVC(train_x, train_y) # current_model=NuSVC(train_x, train_y) current_model = Linearsvc(train_x, train_y) prediction = current_model.predict([X_new]) return iris["target_names"][prediction][0] if __name__ == "__main__": doctest.testmod()