为什么我得到所有10倍的测试精度和平衡精度相同的值?

Question

对于每个折叠测试精度和平衡测试精度不同，但数值是相同的。例如，Fold 1，测试精度为86，平衡测试精度为86。对于Fold 2，测试精度为90，平衡测试精度为90对于折叠3，测试精度为70.555，测试精度为70.555…这是我的代码

fold_no = 1
reports = []
accuracies = []
sensitivities = []
specificities = []
test_accuracy = []
for train, test in kfold.split(X_train, y_train):
model = Sequential()
model.add(Conv3D(128, kernel_size=(3, 3, 3))
model.add(Flatten())
model.add(Dense(256, activation='relu', kernel_regularizer='l2'))
model.add(Dense(4096, activation='relu', kernel_regularizer='l2')) 
model.add(Dropout(0.3))
model.add(Dense(1, activation='sigmoid', kernel_regularizer='l2'))
# Compile the model
model.compile(loss=tensorflow.keras.losses.mean_squared_error,
optimizer=tensorflow.keras.optimizers.Adam(learning_rate=learning_rate),
metrics=['accuracy'])```
history = model.fit(X_train[train], y_train[train],
batch_size=batch_size,
epochs=no_epochs,
verbose=verbosity, validation_data=(X_train[test], y_train[test]))
# Compute the classification report for the testing set
y_pred = model.predict(X_test, verbose = 0)
c = model.evaluate(X_test, y_test)
test_accuracy.append(c[1])
report = classification_report(y_test, (y_pred>0.5), output_dict=True)
from sklearn.metrics import balanced_accuracy_score
bal_acc=balanced_accuracy_score(y_test,(y_pred>0.5))
print("balenced acc is " + str(bal_acc))
# Extract the sensitivity and specificity values from the report
sensitivity = report["1"]["recall"]
specificity = report["0"]["recall"]
sensitivities.append(sensitivity)
specificities.append(specificity)
print(specificity))  
print(sensitivity))

Answer 1

当类一开始是平衡的，平衡精度和精度是相同的:

from sklearn.metrics import accuracy_score, balanced_accuracy_score
y_true = [0, 0, 0, 0, 1, 1, 1, 1]    # 4 negatives, 4 positives
y_pred = [0, 0, 1, 0, 1, 0, 1, 1]
print(accuracy_score(y_true, y_pred), balanced_accuracy_score(y_true, y_pred))
# 0.75 0.75