How to interpret MSE in Keras Regressor

Question

I am new to Keras/TF/Deep Learning and I am trying to build a model to predict house prices.

I have some features X (no. of bathrooms , etc.) and target Y (ranging a

Answer 1

MSE is mean square error, here is the formula.

Basically it is a mean of square of different of expected output and prediction. Making square root of this will not give you the difference between error and output. This is useful for training.

Currently you have build a model. If you want to train the model use these function.

mode.fit(x=input_x_array, y=input_y_array, batch_size=None, epochs=1, verbose=1, callbacks=None, validation_split=0.0, validation_data=None, shuffle=True, class_weight=None, sample_weight=None, initial_epoch=0, steps_per_epoch=None, validation_steps=None)

If you want to do prediction of the output you should use following code.

prediction = model.predict(np.array(input_x_array))
print(prediction)

You can find more details here.

https://keras.io/models/about-keras-models/

https://keras.io/models/sequential/