Evaluating Models

We have learned how to train a regression and classification algorithm in TensorFlow. After this is accomplished, we must be able to evaluate the model's predictions to determine how well it did.

Getting ready

Evaluating models is very important and every subsequent model will have some form of model evaluation. Using TensorFlow, we must build this feature into the computational graph and call it during and/or after our model is training.

Evaluating models during training gives us insight into the algorithm and may give us hints to debug it, improve it, or change models entirely. While evaluation during training isn't always necessary, we will show how to do this with both regression and classification.

After training, we need to quantify how the model performs on the data. Ideally, we have a separate training and test set (and even a validation set) on which we can evaluate the model.

When we want to evaluate a model, we will want to do so on a large batch of data points. If...