Python kNN : KNeighborsRegressor 기초 동작

import

from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsRegressor
from sklearn.metrics import mean_squared_error

import pandas as pd

X, y

X =
y =

train_test_split

X_train, X_test, y_train, y_test =train_test_split(X,y,test_size=0.2,random_state=123)

MinMaxScaler()

scaler = MinMaxScaler()

x_train_scaler = scaler.fit_transform(X_train)
x_test_scaler = scaler.transform(X_test)

KNeighborsRegressor

model_knn = KNeighborsRegressor(n_neighbors=3)

model_knn.fit(x_train_scaler, y_train)
y_pred = model_knn.predict(x_test_scaler)

mean_squared_error(y_test, y_pred)

 MSE -> 예측값과 실제값의 차이를 제곱한 값을 평균한 값, 모델의 예측 정확도를 측정하는 지표, 값이 낮을 수록 모델의 성능이 높다.