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Principal Component Analysis
course content

Course Content

Principal Component Analysis

Principal Component Analysis

1. What is Principal Component Analysis
IntroductionPractical Application of PCAMathematical IdeaExamples of Real ProblemsHow to Explain the Obtained Results?
2. Basic Concepts of PCA
StandardizationCovariance MatrixEigenvalues and EigenvectorsFeature Vector and Principal ComponentsSeeing the Big Picture
3. Model Building
Scikit-learn for PCAExplore DatasetFit Data into the ModelChallenge
4. Results Analysis
Explain Resulting ComponentsWhat’s after?Data CompressionNoise ReductionImage Compression

bookScikit-learn for PCA

We figured out the implementation of the PCA algorithm using the numpy library. Scikit-learn can let us start using this method with just one line of code:

PCA is a scikit-learn library class. It contains more than 5 arguments, but we are most interested in only one - n_components. This argument is responsible for the number of main components that we want to get. The only condition is that the number of components must, of course, be equal to or less than the variables in the dataset. The PCA class contains 2 main methods that we will use: fit and transform. The fit() method loads the data into the class, and the transform() method transforms it, and we get the result of the PCA algorithm. If we want to combine these 2 operations, use the fit_transform() method:

If we want to get the components that the algorithm has calculated, call the .components_ attribute:

Task

Import the PCA class from the scikit-learn library and create a PCA model for the iris dataset with 2 components.

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Section 3. Chapter 1
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bookScikit-learn for PCA

We figured out the implementation of the PCA algorithm using the numpy library. Scikit-learn can let us start using this method with just one line of code:

PCA is a scikit-learn library class. It contains more than 5 arguments, but we are most interested in only one - n_components. This argument is responsible for the number of main components that we want to get. The only condition is that the number of components must, of course, be equal to or less than the variables in the dataset. The PCA class contains 2 main methods that we will use: fit and transform. The fit() method loads the data into the class, and the transform() method transforms it, and we get the result of the PCA algorithm. If we want to combine these 2 operations, use the fit_transform() method:

If we want to get the components that the algorithm has calculated, call the .components_ attribute:

Task

Import the PCA class from the scikit-learn library and create a PCA model for the iris dataset with 2 components.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 3. Chapter 1
toggle bottom row

bookScikit-learn for PCA

We figured out the implementation of the PCA algorithm using the numpy library. Scikit-learn can let us start using this method with just one line of code:

PCA is a scikit-learn library class. It contains more than 5 arguments, but we are most interested in only one - n_components. This argument is responsible for the number of main components that we want to get. The only condition is that the number of components must, of course, be equal to or less than the variables in the dataset. The PCA class contains 2 main methods that we will use: fit and transform. The fit() method loads the data into the class, and the transform() method transforms it, and we get the result of the PCA algorithm. If we want to combine these 2 operations, use the fit_transform() method:

If we want to get the components that the algorithm has calculated, call the .components_ attribute:

Task

Import the PCA class from the scikit-learn library and create a PCA model for the iris dataset with 2 components.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

We figured out the implementation of the PCA algorithm using the numpy library. Scikit-learn can let us start using this method with just one line of code:

PCA is a scikit-learn library class. It contains more than 5 arguments, but we are most interested in only one - n_components. This argument is responsible for the number of main components that we want to get. The only condition is that the number of components must, of course, be equal to or less than the variables in the dataset. The PCA class contains 2 main methods that we will use: fit and transform. The fit() method loads the data into the class, and the transform() method transforms it, and we get the result of the PCA algorithm. If we want to combine these 2 operations, use the fit_transform() method:

If we want to get the components that the algorithm has calculated, call the .components_ attribute:

Task

Import the PCA class from the scikit-learn library and create a PCA model for the iris dataset with 2 components.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 3. Chapter 1
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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