It is time to make some predictions! In Chapter 4, Loading and Preparing the Dataset, we did split the Titanic dataset into two subsets, the training and held-out subsets, respectively consisting of 70% and 30% of the original dataset randomly shuffled. We have used variations of the training subset extensively in chapter 5 Model Creation, to train and select the best classification model. But so far, we have not used the held-out subset at all. In this chapter, we apply our models to this held-out subset to make predictions on unseen data and make a final assessment of the performance and robustness of our models.
Amazon ML offers two types of predictions: batch and streaming. Batch prediction requires a datasource. The samples you want to predict are given to the model all at once in batch mode. Streaming, also known as real-time or online predictions, requires the creation of an API endpoint and consists of submitting sequences of samples, one by...