You're reading from Automated Machine Learning with AutoKeras

Product typeBook

Published inMay 2021

Reading LevelBeginner

PublisherPackt

ISBN-139781800567641

Edition1st Edition

Languages

Python

Tools

Keras

Concepts

Deep Learning

Author (1)

Luis Sobrecueva

Splitting your dataset for training and evaluation

To evaluate a model, you must divide your dataset into three subsets: a training set, a validation set, and a test set. During the training phase, AutoKeras will train your model with the training dataset, while using the validation dataset to evaluate its performance. Once you are ready, the final evaluation will be done using the test dataset.

Why you should split your dataset

Having a separate test dataset that is not used during training is really important to avoid information leaks.

As we mentioned previously, the validation set is used to tune the hyperparameters of your model based on the performance of the model, but some information about the validation data is filtered into the model. Due to this, you run the risk of ending up with a model that works artificially well with the validation data, because that's what you trained it for. However, the actual performance of the model is due to us using previously...

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Automated Machine Learning with AutoKeras

Published in: May 2021Publisher: PacktISBN-13: 9781800567641

Author (1)

Luis Sobrecueva

Luis Sobrecueva is a senior software engineer and ML/DL practitioner currently working at Cabify. He has been a contributor to the OpenAI project as well as one of the contributors to the AutoKeras project.
Read more about Luis Sobrecueva

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You're reading from Automated Machine Learning with AutoKeras

Splitting your dataset for training and evaluation

Why you should split your dataset

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