Python Natural Language Processing

Dimensionality reduction

Dimensionality reduction is a very useful concept in machine learning. If we include a lot of features to develop our ML-model, then sometimes we include features that are really not needed. Sometimes we need high-dimensional features space. What are the available ways to make certain sense about our features space? So we need some techniques that help us remove unnecessary features or convert our high-dimensional features space to two-dimensional or three-dimensional features so that we can see what all is happening. By the way, we have used this concept in Chapter 6, Advance Features Engineering and NLP Algorithms, when we developed an application that generated word2vec for the game of thrones dataset. At that ...