정답: https://towardsdatascience.com/logistic-regression-using-python-sklearn-numpy-mnist-handwriting-recognition-matplotlib-a6b31e2b166a

<aside> 💡

문제

MNIST 데이터셋을 이용해 로지스틱 회귀 모델을 학습시켜 손글씨 숫자를 분류 해봅시다.

</aside>

%matplotlib inline
from sklearn.datasets import load_digits
digits = load_digits()

import numpy as np 
import matplotlib.pyplot as plt

plt.figure(figsize=(20,4))
for index, (image, label) in enumerate(zip(digits.data[0:5], digits.target[0:5])):
    plt.subplot(1, 5, index + 1)
    plt.imshow(np.reshape(image, (8,8)), cmap=plt.cm.gray)
    plt.title('Training: %i\\n' % label, fontsize = 20)

**데이터셋과 테스트셋 분리 (Digits Dataset)**

**인스턴스 생성, 모델 학습, 모델 예측**

score=
print(score)

predictions = logisticRegr.predict(x_test)

import numpy as np 

import seaborn as sns
from sklearn import metrics

cm = metrics.confusion_matrix(y_test, predictions)

plt.figure(figsize=(9,9))
sns.heatmap(cm, annot=True, fmt=".3f", linewidths=.5, square = True, cmap = 'Blues_r');
plt.ylabel('Actual label');
plt.xlabel('Predicted label');
all_sample_title = 'Accuracy Score: {0}'.format(score)
plt.title(all_sample_title, size = 15);
plt.savefig('toy_Digits_ConfusionSeabornCodementor.png')
plt.show()

심화 https://www.kaggle.com/code/hamzaboulahia/logistic-regression-mnist-classification