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“Discriminant analysis, which is often used to make predictions and to classify things, is the core technique in the analysis of high-dimensional datasets. It works by splitting the data into two or more groups that have some relationship between them, allowing us to assign a score to each data point. The problem with this technique is that it is not always as simple as it seems, and there are a number of ways that it can go wrong. One of the biggest problems is that it often involves finding a lot of parameters and making a lot of assumptions about how they relate to each other
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Given text material: Discriminant Analysis, also called Principal Component Analysis (PCA), is a mathematical method used to find linear combinations of features that maximize the variance of observed data, leading to an explanation of those features. Discriminant Analysis is one of the methods of Dimensionality Reduction (DR), which is an essential aspect of Information Retrieval (IR). In IR, it is used to select a set of features, which is a subset of the original dataset, to explain the dataset. This set is called an “Discriminant set”. The
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