I am using balanced_accuracy_score and accuracy_score both in sklearn.metrics.
According to documentation, those two metrics are the same but in my code, the first is giving me 96% and the second one is 97% while accuracy from training is 98%
Can you explain to me what is the difference between the three accuracies and how each is computed?
Note: the problem is a multi-classification problem with three classes.
I have attached code samples.
accuracy is 98%
model.compile(loss='categorical_crossentropy',
optimizer=Adam(lr=0.00001),
metrics=['accuracy'])
accuracy …