SVM(Support Vector Machines) Classifier

별과 삼각형을 분류하기 위해서 가장 넓은 하늘색 공간(maximum marginal hyperplane(MMH))을 찾는 알고리즘이다.

이 공간을 계산하기 위해 인접한 Support Vectors 라 불리는 데이터 포인트를 이용하게 된다.

 

from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn import svm from sklearn.metrics import classification_report, confusion_matrix import pandas as pd import seaborn as sns iris = sns.load_dataset('iris') X_iris = iris.drop(columns=['species'], axis=1) y_iris = iris['species'] # Split dataset into random train and test subsets: Xtrain, Xtest, ytrain, ytest = train_test_split(X_iris, y_iris, random_state=1) # Standardize features by removing mean and scaling to unit variance: scaler = StandardScaler() scaler.fit(Xtrain) Xtrain = scaler.transform(Xtrain) Xtest = scaler.transform(Xtest) classifier = svm.SVC(kernel='linear') classifier.fit(Xtrain, ytrain) # Predict y data with classifier: y_predict = classifier.predict(Xtest) # Print results: print(confusion_matrix(ytest, y_predict)) print(classification_report(ytest, y_predict))