There are lot of dimensionality reduction techniques available in Machine Learning. It is one of the most integral part in Data Science field.Therefore, In this article, I am going to describe one of the most important dimensionality reduction techniques that is being used nowadays,called Principal Component Analysis(PCA).

There are lot of dimensionality reduction techniques available in Machine Learning. It is one of the most integral part in Data Science field.Therefore, In this article, I am going to describe one of the most important dimensionality reduction techniques that is being used nowadays,called Principal Component Analysis(PCA).

But before doing that, one thing we need to know what is Dimensionality Reduction and why it is so important.

Dimensionality Reduction is a technique,used to reduce the dimensions of the feature space.For an example, let’s say, if there are 100 features or columns in a dataset and you want to get only 10 features,using this dimensionality reduction techniques you can achieve this feat. Overall, it transforms the dataset which is in n dimensional space to n’ dimensional space where n’<n.

Dimensionality Reduction is important in machine learning in a lot of ways, but the most important reason above all is the ‘Curse of Dimensionality’.

In machine learning,we often augment as many features as possible at first to get the higher accurate results. However, at a certain point of time,the performance of the model decreases(mainly overfitting) with the increasing number of features. This is the concept of ‘Curse of Dimensionality’.So,this is why dimensionality reduction is very crucial in the field of Machine Learning.

For more information about ‘Curse of Dimensionality’, I am going to mention some of the references at the end of this article.

Now let’s come to the PCA.

PCA is a dimensionality reduction technique that enables us to identify correlations and patterns in a dataset so that it can be transformed into a new dataset of significantly lower dimensionality without the loss of any important information.

Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties of the original data.

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