k-근접 모델(KNeighborsRegressor)_파이썬으로 머신러닝 배우기

 

파이썬으로 머신러닝 배우기 

 

K-NN(nearest neighbor) 모델

K근접모델

 

 

 

1. K-NN알고리즘이란?

 

K-NN알고리즘은 지도 학습 알고리즘 이다. 

 

가장 가까운 이웃끼리 범주화 하는 것을 의미한다. 

 

 

출처 https://bioinformaticsandme.tistory.com/179

 

출처 https://medium.com/@unfinishedgod/r-%EB%A8%B8%EC%8B%A0%EB%9F%AC%EB%8B%9D-knn-%EC%95%8C%EA%B3%A0%EB%A6%AC%EC%A6%98-38925616284c

 

2. K-NN모델 테스트

 

import matplotlib.pyplot as plot
import numpy as np
import math

x = np.random.rand(100,1)
x = x * 10 - 5

y = np.array([math.sin(i) for i in x])
y = y + np.random.randn(100)

from sklearn.neighbors import KNeighborsRegressor

model = KNeighborsRegressor()
model.fit(x, y)

relation_square = model.score(x, y)
print('결정계수 R : ', relation_square)

y_p = model.predict(x)

plot.scatter(x, y, marker = 'x')
plot.scatter(x, y_p, marker = 'o')
plot.show()

 

3. K-NN 인자 조절 

 

neighbors 는 기본적으로 5이다. 

 

n_neighbors

weights = uniform, distance

algorithm {'auto', 'ball_tree', 'kd_tree', 'brute'}

 

import matplotlib.pyplot as plot
import numpy as np
import math

x = np.random.rand(100,1)
x = x * 10 - 5

y = np.array([math.sin(i) for i in x])
y = y + np.random.randn(100)

from sklearn.neighbors import KNeighborsRegressor

for i in (1,3,5,7):
  for j in ('uniform', 'distance'):
    for k in ('auto','ball_tree','kd_tree','brute'):
      model = KNeighborsRegressor(n_neighbors=i, weights=j,algorithm=k)
      model.fit(x, y)

      relation_square = model.score(x, y)
      print('결정계수 R:', relation_square)
    print('\n')
  print('\n')

y_p=model.predict(x)
plot.scatter(x,y,marker='+')
plot.scatter(x,y_p,marker='o')
plot.show()

 

 

 

 

3. K-NN 활용 수학점수로 총점 예측하기

 

import matplotlib.pyplot as plot
import numpy as np
import math
import pandas as pd
import seaborn as sns
import openpyxl
plot.rcParams["font.family"] = 'Malgun gothic'

data = pd.read_excel('student.xlsx', header = 0)

newData = data[['kor', 'eng', 'math', 'social', 'science', 'total']]
#속성(변수)선택
x = newData[['math']]
y = newData[['total']]

from sklearn.neighbors import KNeighborsRegressor

for i in (1,3,5,7):
  for j in ('uniform', 'distance'):
    for k in ('auto','ball_tree','kd_tree','brute'):
      model = KNeighborsRegressor(n_neighbors=i, weights=j,algorithm=k)
      model.fit(x, y)
      relation_square = model.score(x, y)
      print('결정계수 R:', relation_square)
    print('\n')
  print('\n')

y_p=model.predict(x)
plot.scatter(x,y,marker='+')
plot.scatter(x,y_p,marker='o')
plot.show()

 

 

4. 국, 수, 사, 영으로 총점 예측하기

 

 

import matplotlib.pyplot as plot
import numpy as np
import math
import pandas as pd
import seaborn as sns
import openpyxl
plot.rcParams["font.family"] = 'Malgun gothic'

data = pd.read_excel('student.xlsx', header = 0)

newData = data[['kor', 'eng', 'math', 'social', 'science', 'total']]
#속성(변수)선택
x = newData[['kor', 'eng', 'math', 'social']]
y = newData[['total']]

from sklearn.neighbors import KNeighborsRegressor

for i in (1,3,5,7):
  for j in ('uniform', 'distance'):
    for k in ('auto','ball_tree','kd_tree','brute'):
      model = KNeighborsRegressor(n_neighbors=i, weights=j,algorithm=k)
      model.fit(x, y)
      relation_square = model.score(x, y)
      print('결정계수 R:', relation_square)
    print('\n')
  print('\n')

y_p=model.predict(x)
ax1 = sns.distplot(y, hist = False, label = 'y_실제')
ax2 = sns.distplot(y_p, hist = False, label = 'y_예측')

plot.show()