Lesson 7 of 27By Avijeet Biswal
Last updated on Mar 9, 20215259While working with high-dimensional data, machine learning models often seem to overfit, and this reduces the ability to generalize past the training set examples. Hence, it is important to perform dimensionality reduction techniques before creating a model. In this article, we’ll learn the PCA in Machine Learning with a use case demonstration in Python.
Below are the topics that we’ll be covering in this article:
The Principal Component Analysis is a popular unsupervised learning technique for reducing the dimensionality of data. It increases interpretability yet, at the same time, it minimizes information loss. It helps to find the most significant features in a dataset and makes the data easy for plotting in 2D and 3D. PCA helps in finding a sequence of linear combinations of variables.
In the above figure, we have several points plotted on a 2-D plane. There are two principal components. PC1 is the primary principal component that explains the maximum variance in the data. PC2 is another principal component that is orthogonal to PC1.
The Principal Components are a straight line that captures most of the variance of the data. They have a direction and magnitude. Principal components are orthogonal projections (perpendicular) of data onto lower-dimensional space.
Now that you have understood the basics of PCA, let’s look at the next topic on PCA in Machine Learning.
Standardize the data before performing PCA. This will ensure that each feature has a mean = 0 and variance = 1.
Construct a square matrix to express the correlation between two or more features in a multidimensional dataset.
Calculate the eigenvectors/unit vectors and eigenvalues. Eigenvalues are scalars by which we multiply the eigenvector of the covariance matrix.
Now that you have understood How PCA in Machine Learning works, let’s perform a hands-on demo on PCA with Python.
From the above box plots, you can see that some features classify the wine labels clearly, such as Alkalinity, Total Phenols, or Flavonoids.
From the above graph, we’ll consider the first two principal components as they together explain nearly 56% of the variance.
By applying PCA to the wine dataset, you can transform the data so that most we can capture variations in the variables with a fewer number of principal components. It is easier to distinguish the wine classes by inspecting these principal components rather than looking at the raw data.
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The principal component analysis is a widely used unsupervised learning method to perform dimensionality reduction. We hope that this article helped you understand what PCA is and the applications of PCA. You looked at the applications of PCA and how it works.
Do you have any questions related to this article on PCA in Machine Learning? If yes, then please feel free to put them in the comments sections. Our team will be happy to solve your queries. Finally, we performed a hands-on demonstration on classifying wine type by using the first two principal components.
Click on the following video tutorial to learn more about PCA - Principal Component Analysis.
Avijeet is a Senior Research Analyst at Simplilearn. Passionate about Data Analytics, Machine Learning, and Deep Learning, Avijeet is also interested in politics, cricket, and football.
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