Machine-learning-Scikit-learn/train_test_split_method.py at master · samdarshihawk/Machine-learning-Scikit-learn · GitHub

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from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split


X = digits.data
y = digits.target


X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=30, stratify=y)


knn = KNeighborsClassifier(n_neighbors=7)


knn.fit(X_train,y_train)


print(knn.score(X_test, y_test))


neighbors = np.arange(1, 9)
train_accuracy = np.empty(len(neighbors))
test_accuracy = np.empty(len(neighbors))


for i, k in enumerate(neighbors):
    # Setup a k-NN Classifier with k neighbors: knn
    knn = KNeighborsClassifier(n_neighbors=k)


    knn.fit(X_train,y_train)


    train_accuracy[i] = knn.score(X_train, y_train)


    test_accuracy[i] = knn.score(X_test, y_test)

# Generate plot
plt.title('k-NN: Varying Number of Neighbors')
plt.plot(neighbors, test_accuracy, label = 'Testing Accuracy')
plt.plot(neighbors, train_accuracy, label = 'Training Accuracy')
plt.legend()
plt.xlabel('Number of Neighbors')
plt.ylabel('Accuracy')
plt.show()