Why You Should Use Cross-Entropy Error Instead Of Classification Error Or Mean Squared Error For Neural Network Classifier Training

James D. McCaffrey

When using a neural network to perform classification and prediction, it is usually better to use cross-entropy error than classification error, and somewhat better to use cross-entropy error than mean squared error to evaluate the quality of the neural network. Let me explain. The basic idea is simple but there are a lot of related issues that greatly confuse the main idea. First, let me make it clear that we are dealing only with a neural network that is used to classify data, such as predicting a person’s political party affiliation (democrat, republican, other) from independent data such as age, sex, annual income, and so on. We are not dealing with a neural network that does regression, where the value to be predicted is numeric, or a time series neural network, or any other kind of neural network.

Now suppose you have just three training data items. Your neural network…

View original post 953 more words

Advertisements

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Google photo

You are commenting using your Google account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s