혼동 행렬

import os
os.environ["KERAS_BACKEND"] = "torch"
import keras
(x_train, y_train), (x_test, y_test) = keras.datasets.fashion_mnist.load_data()

x_train_binary, y_train_binary = filter_binary(x_train, y_train, 2, 6)
x_test_binary, y_test_binary = filter_binary(x_test, y_test, 2, 6)

model = keras.models.Sequential(
    [
        keras.layers.Rescaling(1/255),
        keras.layers.Flatten(),
        keras.layers.Dense(1, activation='sigmoid')
    ]
)
model.compile(optimizer=keras.optimizers.SGD(learning_rate=0.001),
              loss='binary_crossentropy', metrics=['accuracy'])
model.fit(x_train_binary, y_train_binary, epochs=5, batch_size=32)

import numpy as np
y_prob = model.predict(x_test_binary) # 예측 확률
threshold = 0.5 # 문턱값
y_pred = np.where(y_prob >= threshold, 1, 0) # 문턱값보다 크면 1 아니면 0

from sklearn.metrics import * # sklearn.metrics 모듈에서 모든 함수 불러오기
y_true = y_test_binary # 실제 정답
confusion_matrix(y_true, y_pred) # 혼동행렬(정답, 예측 순)

accuracy_score(y_true, y_pred)

precision_score(y_true, y_pred)

recall_score(y_true, y_pred)

recall_score(y_true, y_pred, pos_label=0)

f1_score(y_true, y_pred)