Java Deep Learning Cookbook

How it works...

Before we start schema creation, we need to examine all the features in our dataset. Then, we need to clear all the noisy features, such as name, where it is fair to assume that they have no effect on the produced outcome. If some features are unclear to you, just keep them as such and include them in the schema. If you remove a feature that happens to be a signal unknowingly, then you'll degrade the efficiency of the neural network. This process of removing outliers and keeping signals (valid features) is referred to in step 1. Principal Component Analysis (PCA) would be an ideal choice, and the same has been implemented in ND4J. The PCA class can perform dimensionality reduction in the case of a dataset with a large number ...