๐Ÿ† ์ž๊ฒฉ์ฆ, ์–ดํ•™

[๋น…๋ฐ์ดํ„ฐ ๋ถ„์„๊ธฐ์‚ฌ] ์‹ค๊ธฐ 7ํšŒ - 3์œ ํ˜• ๋‹ค์ค‘ํšŒ๊ท€๋ถ„์„

๋ฐ์ดํ„ฐํŒ์Šค 2024. 8. 21. 18:02

 

๋ฌธ์ œ : ```{admonition} 3-2

**Target ๋ณ€์ˆ˜๋ฅผ ์ข…์†๋ณ€์ˆ˜๋กœ ํ•˜์—ฌ ๋‹ค์ค‘์„ ํ˜•ํšŒ๊ท€๋ชจ๋ธ๋ง์„ ์ง„ํ–‰ํ–ˆ์„ ๋•Œ v2 ์ปฌ๋Ÿผ์˜ ํšŒ๊ท€ ๊ณ„์ˆ˜๋Š”?**

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3์œ ํ˜• ์‰ฝ๋‹ค๊ณ  ์ƒ๊ฐํ•œ ๋‚˜ ์ž์‹ ์„ ๋ฐ˜์„ฑํ•œ๋‹ค ใ…‡ใ…‡..

import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
# x ์ง€์ • ํ•„์ˆ˜
y=df['Target']
x=df.drop(columns=['Target'])
model=LinearRegression()
model.fit(x,y)

print(np.round(model.coef_,2))
print(np.round(model.coef_[1],2))
 

๋‹ค์ค‘์„ ํ˜•ํšŒ๊ท€๋ถ„์„์„ ํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š”

  1. ๋…๋ฆฝ๋ณ€์ˆ˜์™€ ์ข…์†๋ณ€์ˆ˜๋ฅผ ๋จผ์ € ์ง€์ •ํ•ด์ค˜์•ผ ํ•œ๋‹ค
  2. sklearn.linear_model from LinearRegression
  3. ๋ชจ๋ธ๋ฅผ ์„ค์ •ํ•ด์ฃผ๊ณ  x,y๋ฅผ ํ•ํ•ด์ค€๋‹ค
  4. coef ๊ฐ’์—์„œ v2 = 1๋ฒˆ์งธ ์ธ๋ฑ์Šค์˜ ๊ฐ’ ์ด๋ฏ€๋กœ model_cef_[1]๋ฅผ ์จ์•ผํ•จ!!

 

import statsmodels.api as sm
y=df['Target']
x=df.drop(columns=['Target'])
x=sm.add_constant(x)
model=sm.OLS(y,x).fit()
summary=model.summary()
print(summary)
 

statsmodel๋กœ ํ‘ธ๋Š” ๋ฐฉ๋ฒ•