I have a dataset of different people with their insurance cost. I have trained a neural-network to predict the insurance cost (charges column) based on the other features (age,bmi, etc.). Here is how my nn works:
insurance_model.compile( loss=tf.keras.losses.mae, optimizer=tf.keras.optimizers.Adamax(learning_rate=0.001), metrics=["mae"] )
Here are some of the statistics of my target feature (charges column)
and here is the histogram of the values of charges column:
I evaluate my neural-network based on mae (mean-absolute-error) and at the end my mae=1300 for both train and test datasets.
So, I am wondering, if 1300 is good enough ?
How can i know, if any result is good enough ?