To understand how to implement principal component analysis, let’s use a simple dataset. In this tutorial, we’ll use the wine dataset available as part of scikit-learn'sdatasetsmodule. Let’s start by loading and preprocessing the dataset: from sklearn import datasets wine_data = datasets.l...
Importantly, thedataseton which PCA technique is to be used must be scaled. The results are also sensitive to the relative scaling. As a layman, it is a method of summarizing data. Imagine some wine bottles on a dining table. Each wine is described by its attributes like colour, strength,...
+ + + Args: + dataset (`torch.utils.data.Dataset`): The dataset to be loaded. + shuffle (bool, optional): Whether to shuffle the dataset. Defaults to False. + seed (int, optional): Random worker seed for sampling, defaults to 1024. + add_sampler: Whether to add ``Distributed...