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Machine Learning for Healthcare Analytics Projects

You're reading from   Machine Learning for Healthcare Analytics Projects Build smart AI applications using neural network methodologies across the healthcare vertical market

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Product type Paperback
Published in Oct 2018
Publisher Packt
ISBN-13 9781789536591
Length 134 pages
Edition 1st Edition
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Author (1):
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Eduonix Learning Solutions Eduonix Learning Solutions
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Eduonix Learning Solutions
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A comparison of categorical and binary problems

We will compare and contrast our categorical classification problem and the binary classification problem just covered. To do this, first we have to create a new model, since we've changed our data. We will define a binary model, then we will define an input layer, a hidden layer and an output layer, compile the model, and finally print the model:

As we see in the screenshot, our third layer has only one output value, so it's going to be 0 and 1, instead of a one-hot encoded vector for a categorical classification. So, our binary model is ready, and now we're in the training phase—let's fit the binary model to our binary data that we curated:

As we can see in the preceding screenshot, we're getting better accuracy than we were on our categorical classification problem. Binary classification is like...

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