how to adjust accuracy only for 1's?

Question

Suppose I have such data :

x1      x2     x3       y
0.85   0.95   0.22      1
0.35   0.26   0.42      0
0.89   0.82   0.82      1
0.36   0.14   0.32      0
0.44   0.53   0.82      1
0.75   0.78   0.52      1

I predict binary classification but the only thing that matters ,is the correct prediction of the 1s, and if the prediction is 0, it will not affect my accuracy.

I simply used the following code :

model.compile(optimizer=keras.optimizers.Adam(learning_rate=0.001),
              loss='binary_crossentropy',
              metrics=['accuracy'])

But this code also includes zeros in its accuracy.

How can I apply to the network that only the prediction of 1 is important ?

In other words, During fitting model, if the prediction was zero , this zero predication does not apply to the model accuracy.

Answer 1

It looks like you care about precision of the model. Precision means for all instances that you predict 1, what portion of them is correct.

If yes, use tf.keras.metrics.Precision() as metrics.

model.compile(optimizer=keras.optimizers.Adam(learning_rate=0.001),
    loss='binary_crossentropy',
    metrics=[tf.keras.metrics.Precision()])