Dimensionality Reduction

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Curse Of Dimensionality

What is Dimensionality Reduction ?

Why Dimensionality Reduction is important ?

Techniques to overcome the Curse of Dimensionality ?

Two dimensional data points reduced to one dimensional data points.


Missing Value Ratio:

Low Variance Filter:

Backward Feature Elimination:

Forward Feature Construction:

High Correlation Filter:

Note :

Understanding Principal Component Analysis:

Loading the Libraries and image dataset. Getting the Dimension of the image.
Viewing the image dataset
Fitting the Random Forest on the reduced number of the principal components. We are iterating through variable number of components to find best number of Principal Components.
Results of accuracy with respect to the components and amount variance explained by each component.
Amount of variance explained by 32 component can viewed by the plot.

Machine Learning Engineer | Avid Reader | Movie Buff | https://mayurji.github.io/