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:
![](https://static.packt-cdn.com/products/9781789536591/graphics/assets/56994eee-ca98-426a-bf87-783336608521.png)
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:
![](https://static.packt-cdn.com/products/9781789536591/graphics/assets/b895e419-dd26-44b2-ba21-b8bfb5a66876.png)
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...