Closed mattpearson closed 6 years ago
Hello,
Nope, I don' think you have to do any reverse scaling for predicted y, because you are using original values of y for training.
Which means that you only perform preprocessing on input x. Let's take image classification as an example:
mean_image = np.mean(X_train, axis = 0)
X_train -= mean_image
X_val -= mean_image
X_test -= mean_image
Then you use this transformed data as your input to the model. So, train an f such that f(transformed_x) = value_y.
FYI: CS231n
Greetings. Well done re-implementation of the paper. Had a quick comment/question on your use of a scalar and predicting the result. Agree that it indeed the input data should be scaled prior to estimation, however shouldn't you inverse-transform the prediction back to its original scale before you assess how it compared to the benchmark? Relevant code is below. Thank you.