혼동 행렬

import tensorflow as tf
(x_train, y_train), (x_test, y_test) = \
    tf.keras.datasets.fashion_mnist.load_data()

import numpy as np
def filter_dataset(x, y, neg_cls, pos_cls):
    m = (y == pos_cls) | (y == neg_cls)
    x = x[m]
    y = y[m]
    y = np.where(y == neg_cls, 0, 1)
    return x, y

x_train2, y_train2 = filter_dataset(x_train, y_train, 0, 6)

from tensorflow.keras.layers import *

model = tf.keras.Sequential([
    Flatten(),
    Dense(1, activation='sigmoid'),
])
model.compile(loss='binary_crossentropy', metrics=['accuracy'])
model.fit(x_train2, y_train2)

import numpy as np
x_test2, y_test2 = filter_dataset(x_test, y_test, 0, 6)
prob = model.predict(x_test2)
threshold = 0.5
y_pred = np.where(prob > threshold, 1, 0)

from sklearn.metrics import *

confusion_matrix(y_test2, y_pred)

accuracy_score(y_test2, y_pred)

precision_score(y_test2, y_pred)

recall_score(y_test2, y_pred)

from sklearn.metrics import roc_auc_score, roc_curve
import matplotlib.pyplot as plt

fpr, tpr, threshold = roc_curve(y_test2, prob)
plt.plot(fpr, tpr)

roc_auc_score(y_test2, prob)