[zerobase_데이터 취업스쿨_스터디노트] 46. 정밀도와 재현율

정밀도와 재현율 (precision & recall) 의 트레이드오프

와인 데이터셋 불러오기

import pandas as pd

red_url = 'https://raw.githubusercontent.com/PinkWink/ML_tutorial/master/dataset/winequality-red.csv'

white_url = 'https://raw.githubusercontent.com/PinkWink/ML_tutorial/master/dataset/winequality-white.csv'

red_wine =pd.read_csv(red_url, sep=';')
white_wine =pd.read_csv(white_url, sep=';')

red_wine['color'] = 1. 
white_wine['color'] = 0. 

wine = pd.concat([red_wine, white_wine])
wine['taste'] = [1. if grade > 5 else 0. for grade in wine['quality']]

X = wine.drop(['taste', 'quality'], axis=1)
y = wine['taste']

데이터 나누기

from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score

X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.2, random_state=13)

학습과 예측값 및 정확도

from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

lr = LogisticRegression(solver='liblinear', random_state=13)
lr.fit(X_train, y_train)

y_pred_tr = lr.predict(X_train)
y_pred_test = lr.predict(X_test)

print('Train Acc:', accuracy_score(y_train, y_pred_tr))
print('Test Acc: ', accuracy_score(y_test, y_pred_test))

Train Acc: 0.7425437752549547
Test Acc: 0.7438461538461538

classification_report - y_test

from sklearn.metrics import classification_report

print(classification_report(y_test, lr.predict(X_test)))

          precision    recall  f1-score   support

     0.0       0.68      0.58      0.62       477
     1.0       0.77      0.84      0.81       823

accuracy                           0.74      1300

macro avg 0.73 0.71 0.71 1300
weighted avg 0.74 0.74 0.74 1300

precision, recall, threshold 시각화

import matplotlib.pyplot as plt
from sklearn.metrics import precision_recall_curve

plt.figure(figsize=(10,8))
pred = lr.predict_proba(X_test)[:, 1]

precisions, recalls, thresholds = precision_recall_curve(y_test, pred)

plt.plot(thresholds, precisions[:len(thresholds)], label='precisions')
plt.plot(thresholds, recalls[:len(thresholds)], label='recalls')
plt.grid()
plt.legend()
plt.show()

# thredshold = 0.5

pred_proba = lr.predict_proba(X_test)
pred_proba[:3]

array([[0.40489885, 0.59510115],
[0.51046968, 0.48953032],
[0.10197579, 0.89802421]])

concatenate

import numpy as np

np.concatenate([pred_proba, y_pred_test.reshape(-1,1)], axis=1)

array([[0.40489885, 0.59510115, 1. ],
[0.51046968, 0.48953032, 0. ],
[0.10197579, 0.89802421, 1. ],
...,
[0.22543326, 0.77456674, 1. ],
[0.67255092, 0.32744908, 0. ],
[0.31413623, 0.68586377, 1. ]])

y_pred_test

array([1., 0., 1., ..., 1., 0., 1.])

y_pred_test.reshape(-1,1)

array([[1.],
[0.],
[1.],
...,
[1.],
[0.],
[1.]])

threshold 바꿔보기 - Binarizer

from sklearn.preprocessing import Binarizer

binarizer = Binarizer(threshold=0.6).fit(pred_proba)

pred_bin = binarizer.transform(pred_proba)[:,1]
pred_bin

array([0., 0., 1., ..., 1., 0., 1.])

from sklearn.metrics import classification_report

print(classification_report(y_test, pred_bin))

[0.5] 일 때

          precision    recall  f1-score   support

     0.0       0.68      0.58      0.62       477
     1.0       0.77      0.84      0.81       823

accuracy                           0.74      1300

macro avg 0.73 0.71 0.71 1300
weighted avg 0.74 0.74 0.74 1300

[0.6] 일 때

          precision    recall  f1-score   support

     0.0       0.62      0.73      0.67       477
     1.0       0.82      0.74      0.78       823

accuracy                           0.74      1300

macro avg 0.72 0.73 0.72 1300
weighted avg 0.75 0.74 0.74 1300

recall 이 향상 된 것을 볼 수 있다.