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...
