KNN( K-Nearest Neightbors ) Classification
- 정의
- KNN regression과 같지만 차이점은 범주형라벨을 이용하여 분류를 한다는 점
- 참조 : KNN regression ( 링크달기 )
- Python Example
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.neighbors import KNeighborsClassifier
# list for column headers
names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
# open file with pd.read_csv
dataset = pd.read_csv("https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv", names=names)
print(dataset.shape)
# print head of data set
dataset.head()
# split data into X and y
X = dataset.iloc[:,0:8]
Y = dataset.iloc[:,8]
# split data into train and test sets
seed = 1
test_size = 0.33
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=test_size, random_state=seed)
neigh = KNeighborsClassifier(n_neighbors=5)
neigh.fit(X_train, y_train)
predicted_values = neigh.predict(X_test)
# evaluate predictions
accuracy = accuracy_score(y_test, predicted_values)
print("Accuracy: %.2f%%" % (accuracy * 100.0))
참고 자료
https://medium.com/machine-learning-101/k-nearest-neighbors-classifier-1c1ff404d265
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