how to measure the accuracy of knn classifier in python

I have used knn to classify my dataset. But I do not know how to measure the accuracy of the trained classifier. Does scikit have any inbuilt function to check accuracy of knn classifier?

from sklearn.neighbors import KNeighborsClassifier knn = KNeighborsClassifier() knn.fit(training, train_label) predicted = knn.predict(testing) 

Appreciate all the help. Thanks

3 Answers

Use sklearn.metrics.accuracy_score:

acc = accuracy_score(test_label, predicted) 

You can use this code to getting started straight forward. It uses IRIS dataset. There are 3 classes available in iris dataset, Iris-Setosa, Iris-Virginica, and Iris-Versicolor.

Use this code. This gives me 97.78% accuracy

from sklearn import neighbors, datasets, preprocessing from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score from sklearn.metrics import classification_report from sklearn.metrics import confusion_matrix iris = datasets.load_iris() X, y = iris.data[:, :], iris.target Xtrain, Xtest, y_train, y_test = train_test_split(X, y, stratify = y, random_state = 0, train_size = 0.7) scaler = preprocessing.StandardScaler().fit(Xtrain) Xtrain = scaler.transform(Xtrain) Xtest = scaler.transform(Xtest) knn = neighbors.KNeighborsClassifier(n_neighbors=3) knn.fit(Xtrain, y_train) y_pred = knn.predict(Xtest) print(accuracy_score(y_test, y_pred)) print(classification_report(y_test, y_pred)) print(confusion_matrix(y_test, y_pred)) 

Another option is to calculate the confusion matrix, which tells you the accuracy of both classes and the alpha and beta errors:

from sklearn.metrics import confusion_matrix con_mat = confusion_matrix(true_values, pred_values, [0, 1]) 

In case your labels are 0 and 1. If you want a nice output, you can add this code:

from numpy import np import math total_accuracy = (con_mat[0, 0] + con_mat[1, 1]) / float(np.sum(con_mat)) class1_accuracy = (con_mat[0, 0] / float(np.sum(con_mat[0, :]))) class2_accuracy = (con_mat[1, 1] / float(np.sum(con_mat[1, :]))) print(con_mat) print('Total accuracy: %.5f' % total_accuracy) print('Class1 accuracy: %.5f' % class1_accuracy) print('Class2 accuracy: %.5f' % class2_accuracy) print('Geometric mean accuracy: %.5f' % math.sqrt((class1_accuracy * class2_accuracy)))