03wk-12: 취업 / 로지스틱 – 추가해설

Author

최규빈

Published

September 21, 2023

1. 강의영상

2. Imports

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sklearn.linear_model

3. 데이터 불러오기

df_train = pd.read_csv('https://raw.githubusercontent.com/guebin/MP2023/main/posts/employment.csv')
df_train
toeic gpa employment
0 135 0.051535 0
1 935 0.355496 0
2 485 2.228435 0
3 65 1.179701 0
4 445 3.962356 1
... ... ... ...
495 280 4.288465 1
496 310 2.601212 1
497 225 0.042323 0
498 320 1.041416 0
499 375 3.626883 1

500 rows × 3 columns

plt.plot(df_train.toeic,df_train.gpa,'o')
df_filtered = df_train[df_train.employment==1]
plt.plot(df_filtered.toeic,df_filtered.gpa,'o')

4. 분석

A. 데이터 정리

X = pd.get_dummies(df_train[['toeic','gpa']])
y = df_train[['employment']]
X
toeic gpa
0 135 0.051535
1 935 0.355496
2 485 2.228435
3 65 1.179701
4 445 3.962356
... ... ...
495 280 4.288465
496 310 2.601212
497 225 0.042323
498 320 1.041416
499 375 3.626883

500 rows × 2 columns

B. Predictor (\(\star\star\star\))

- 여기가 중요함. \(y\)가 연속형이 아니라 범주형으로 이루어진 경우는 sklearn.linear_model.LogisticRegression() 이용하여 predictor를 만들 것

predictr = sklearn.linear_model.LogisticRegression()

C. 학습

predictr.fit(X,y)
/home/cgb2/anaconda3/envs/ag/lib/python3.10/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
LogisticRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

D. 예측

predictr.predict(X) 
array([0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1,
       1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1,
       0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0,
       0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0,
       1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0,
       1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1,
       0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1,
       1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1,
       1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1,
       0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0,
       1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0,
       0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0,
       0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1,
       0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0,
       0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1,
       0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1,
       0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1,
       0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0,
       0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1,
       1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1,
       0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0,
       0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1])

E. 평가

predictr.score(X,y)
0.882
(predictr.predict(X) == y.employment).mean()
0.882
plt.plot(df_train.toeic,df_train.gpa,'o')
df_filtered = df_train[predictr.predict(X)==1]
plt.plot(df_filtered.toeic,df_filtered.gpa,'o')

  • 이 정도면 합리적임
2023-10-24 추가해설

오버피팅: 물론 정확률이 좀 아쉽긴하지만 여기서 더 맞추려고 한다면 오버핏이다. 모형을 개선하는게 의미가 없을것 같다는 판단을 해야함.